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The feasibility of using charge transfer devices in the classification of multispectral imagery was investigated by evaluating particular devices to determine their suitability in matrix multiplication subsystem of a pattern classifier and by designing a protype of such a system. Particular attention was given to analog-analog correlator devices which consist of two tapped delay lines, chip multipliers, and a summed output. The design for the classifier and a printed circuit layout for the analog boards were completed and the boards were fabricated. A test j:g for the board was built and checkout was begun.

The feasibility of using charge transfer devices in the classification of multispectral imagery was investigated by evaluating particular devices to determine their suitability in matrix multiplication subsystem of a pattern classifier and by designing a protype of such a system. Particular attention was given to analog-analog correlator devices which consist of two tapped delay lines, chip multipliers, and a summed output. The design for the classifier and a printed circuit layout for the analog boards were completed and the boards were fabricated. A test j:g for the board was built and checkout was begun.

Thesis (MS)?Naval Postgraduate School, 1964

A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of-sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing.

This article is from PLoS ONE , volume 9 . Abstract The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.

Statistical methods for pattern recognition and classification applications

Electrocardiogram (ECG) reflects activity of the central of the blood circulatory system, i.e. the heart. An ECG signal can provide us with a great deal of information on the normal and pathological physiology of heart activity. Thus, ECG is an important non-invasive clinical tool for the diagnosis of heart diseases.          According to the medical definition the most important information in the ECG signal is concentrated in the P wave, QRS complex and T wave. These data include positions and/or magnitudes of the QRS interval, PR interval, QT interval, ST interval, PR segment, and ST segment (see Fig. 1). Based on the above data, doctors can correctly diagnose human heart diseases. Therefore, analyzing the ECG signals of cardiac arrhythmia is very important for doctors to make correct clinical diagnoses. In order to perform ECG signals classification of the cardiac arrhythmia, the first important task is to determine an appropriate set of features. The feature selection method which chooses the best features from original features to have the maximum recognition rate, simplify classified computation and comprehend the causal relation of classified question. Signal Processing   is undoubtedly the best real time implementation of a specific problem. Wavelet Transform is a very powerful technique for feature extraction and can be used along with neural network structures to build computationally efficient models for diagnosis of Biosignals (ECG in this case). This work utilizes the above techniques for diagnosis of an ECG signal by determining its nature as well as exploring the possibility for real-time implementation of the above model. Daubechies wavelet transform and multi-layered perceptron are the computational techniques used for the realization of the above model. The ECG signals were obtained from the MIT-BIH arrhythmia database and are used for the identification of four different types of arrhythmias. The identification was implemented real-time in SIMULINK, to simulate the detection model under test condition and verify its workability.

In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.

In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.

In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.

In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.

This article is from Healthcare Informatics Research , volume 16 . Abstract Objectives: This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods: For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results: We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions: Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers.

Patterndassification and clustering algorithms are key components of modern information processing systems used to perform tasks such as speech and image recognition, printedcharacter recognition, medical diagnosis, fault detection, process control, and financial decision making. To simplifY the task of applying these types of algorithms in new application areas, we have developed LNKnet-a software package that provides access toinore than 20 patternclassification, clustering, and featureselection algorithms. Included are the most important algorithms from the fields of neural networks, statistics, machine learning, and artificial intelligence. The algorithms can be trained and tested on separate data or tested with automatic crossvalidation. LNKnet runs under the UNIX operating system and access to the different algorithms is provided through a graphical pointandclick user interface. Graphical outputs include twodimensional (2D) scatter and decisionregion plots and 1-D plots of data histograms, classifier outputs, and error rates during training. Parameters of trained classifiers are stored in files from which the parameters can be translated into source-code subroutines (written in the C programming language) that can then be embedded in a user application program. Lincoln Laboratory and other research laboratories have used LNKnet successfully for many diverse applications.

A separation algorithm applicable to the pattern classification and cluster analysis of n-dimensional (n > 2) data is presented. The algorithm reduces the dimensionality of the problem by projecting each point into a plane. This plane is presented to the user on a computer graphics console screen. The operator picks a point on the screen with a lightpen and chooses a \"direction of movement\" to achieve or increase separation, thereby causing an iteration of the algorithm. Each iteration is in fact a reorientation of the plane into which the data points are projected. Iterations continue until satisfactory separation is achieved. The algorithm is not restricted by the dimensionality of the data, nor are any distributional assumptions required. Results from six case studies indicate that the algorithm is a useful tool for the analysis of multidimensional data.

This paper designs an efficient two-class pattern classifier utilizing asynchronous cellular automata (ACAs). The two-state three-neighborhood one-dimensional ACAs that converge to fixed points from arbitrary seeds are used here for pattern classification. To design the classifier, we first identify a set of ACAs that always converge to fixed points from any seeds with following properties - (1) each ACA should have at least two but not huge number of fixed point attractors, and (2) the convergence time of these ACAs are not to be exponential. In order to address the first issue, we propose a graph, coined as fixed point graph of an ACA that facilitates in counting the fixed points. We further perform an experimental study to estimate the convergence time of ACAs, and find that there are some convergent ACAs which demand exponential convergence time. Finally, we find that there are 71 (out of 256) ACAs which can be effective candidates as pattern classifier. We use each of the candidate ACAs on some standard data sets, and observe the effectiveness of each ACAs as pattern classifier. It is observed that the proposed classifier is very competitive and performs reliably better than many standard existing algorithms.

This paper designs an efficient two-class pattern classifier utilizing asynchronous cellular automata (ACAs). The two-state three-neighborhood one-dimensional ACAs that converge to fixed points from arbitrary seeds are used here for pattern classification. To design the classifier, we first identify a set of ACAs that always converge to fixed points from any seeds with following properties - (1) each ACA should have at least two but not huge number of fixed point attractors, and (2) the convergence time of these ACAs are not to be exponential. In order to address the first issue, we propose a graph, coined as fixed point graph of an ACA that facilitates in counting the fixed points. We further perform an experimental study to estimate the convergence time of ACAs, and find that there are some convergent ACAs which demand exponential convergence time. Finally, we find that there are 71 (out of 256) ACAs which can be effective candidates as pattern classifier. We use each of the candidate ACAs on some standard data sets, and observe the effectiveness of each ACAs as pattern classifier. It is observed that the proposed classifier is very competitive and performs reliably better than many standard existing algorithms.

The classification of multicomponent petroleum oils (crude oils, lubricants, distillate and residual fuels) solely by their infrared absorption spectra is a difficult task. Crude oils alone include a phenomenal variety of systems, from heavy asphaltic crudes to light crudes that are similar to a No. 2 fuel oil. Furthermore, the distinctions between classes of fuel oils (i.e., Nos. 1, 2, 4, 5 and 6 fuels) are based upon ASTM specifications for continuous properties such as flash point and viscosity. In South Florida, for example, local fuel oil suppliers meet requests for Nos. 4 or 5 fuel oils by blending together appropriate proportions of Nos. 2 and 6 fuels. In order to reduce the amount of sampling required in the event of an oil pollution incident, it would be useful to be able to initially classify the pollution sample into one of the above groups. Infrared spectroscopy has been promoted as a useful analytical technique for oil classification and identifications, since it does provide some information on the aliphatic, aromatic, polynuclear aromatic, carbonyl, and organosulfur composition of an oil. Infrared spectra have been used in previous efforts to distinguish asphalts from residual fuels, and to provide a tool for 'fingerprinting' oils. Kawahara et al. applied linear discriminant function analysis (LDFA) to their infrared data to make the binary distinction between asphalts and residual fuels. (Author)

This project was completed to determine if fuzzy set classification could be used to accurately evaluate data collected during a psychophysiological detection of deception examination. This methodology provides an alternative to the proprietary statistical technique now commonly used. Data collected using both the Modified General Question Technique (MGQT) and the Relevant Only formats were evaluated. An extensive and, arguably, complete set of polygraph data features was identified. These polygraph data features were not individual dependent, examiner dependent, or in any way dependent on apriori or posteriori knowledge (statistics) of the data. A fuzzy K-Nearest Neighbor classifier and an adaptive fuzzy Least Mean Squares classifier were developed. A fuzzy C-Means clustering algorithm which enabled visualization of the data features was also developed. The fuzzy algorithms were 'forced' to make a choice of truth versus deception; they could, however, be used to return a number that would, in near real-time, give the examiner an idea of the confidence level of the algorithm. The data were parsed such that 25% of the data were tested using an algorithm developed from the remaining 75% of the data. It is shown that only four features are needed to achieve 100% correct classification of the Relevant Only data and 97% correct classification of the MGQT data. It is suggested that any future research development, or testing or computer classification techniques, including statistical and neural techniques include the results of this work.

This project was completed to determine if fuzzy set classification could be used to accurately evaluate data collected during a psychophysiological detection of deception examination. This methodology provides an alternative to the proprietary statistical technique now commonly used. Data collected using both the Modified General Question Technique (MGQT) and the Relevant Only formats were evaluated. An extensive and, arguably, complete set of polygraph data features was identified. These polygraph data features were not individual dependent, examiner dependent, or in any way dependent on apriori or posteriori knowledge (statistics) of the data. A fuzzy K-Nearest Neighbor classifier and an adaptive fuzzy Least Mean Squares classifier were developed. A fuzzy C-Means clustering algorithm which enabled visualization of the data features was also developed. The fuzzy algorithms were 'forced' to make a choice of truth versus deception; they could, however, be used to return a number that would, in near real-time, give the examiner an idea of the confidence level of the algorithm. The data were parsed such that 25% of the data were tested using an algorithm developed from the remaining 75% of the data. It is shown that only four features are needed to achieve 100% correct classification of the Relevant Only data and 97% correct classification of the MGQT data. It is suggested that any future research development, or testing or computer classification techniques, including statistical and neural techniques include the results of this work.

Currently, the United States Navy performs routine intrusive maintenance on CH-46 helicopter gearboxes in order to diagnose and correct possible fault condition. (incipient fault) which could eventually lead to gearbox failure. This type of preventative maintenance is costly and it decreases mission readiness by temporarily grounding usable helicopter. Non-invasive detection of these fault conditions would save tine and prove cost-effective in both manpower and materials. This research deals with the development of a non-invasive fault detector through a combination of digital signal processing and artificial neural network (ANN) technology. The detector will classify incipient faults based on real-tine vibration data taken from the gearbox itself. Neural networks are systems of interconnected units that are trained to compute a specific output as a non-linear function of their inputs. For sons tine the United States Navy has been interested in the use of artificial neural networks in monitoring the health of helicopter gearboxes. In order to determine the detection sensitivity of this method in comparison with traditional invasive methods, the USN funded Westland Helicopters Ltd to conduct a series of CH-46 gearbox rig tests. In these tests, the gearbox was seeded with nine different fault conditions. This seeded fault testing provided the vibration data necessary to develop and test the feasibility of en artificial neural network for fault classification. This research deals with the formation of the pattern vectors to be used in the neural network classifier, the construction of the classification network, and an analysis of results.

This paper presents a spike-based model which employs neurons with functionally distinct dendritic compartments for classifying high dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before being linearly integrated at the soma, giving the neuron a capacity to perform a large number of input-output mappings. The model utilizes sparse synaptic connectivity; where each synapse takes a binary value. The optimal connection pattern of a neuron is learned by using a simple hardware-friendly, margin enhancing learning algorithm inspired by the mechanism of structural plasticity in biological neurons. The learning algorithm groups correlated synaptic inputs on the same dendritic branch. Since the learning results in modified connection patterns, it can be incorporated into current event-based neuromorphic systems with little overhead. This work also presents a branch-specific spike-based version of this structural plasticity rule. The proposed model is evaluated on benchmark binary classification problems and its performance is compared against that achieved using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) techniques. Our proposed method attains comparable performance while utilizing 10 to 50% less computational resources than the other reported techniques.

This paper presents a spike-based model which employs neurons with functionally distinct dendritic compartments for classifying high dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before being linearly integrated at the soma, giving the neuron a capacity to perform a large number of input-output mappings. The model utilizes sparse synaptic connectivity; where each synapse takes a binary value. The optimal connection pattern of a neuron is learned by using a simple hardware-friendly, margin enhancing learning algorithm inspired by the mechanism of structural plasticity in biological neurons. The learning algorithm groups correlated synaptic inputs on the same dendritic branch. Since the learning results in modified connection patterns, it can be incorporated into current event-based neuromorphic systems with little overhead. This work also presents a branch-specific spike-based version of this structural plasticity rule. The proposed model is evaluated on benchmark binary classification problems and its performance is compared against that achieved using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) techniques. Our proposed method attains comparable performance while utilizing 10 to 50% less computational resources than the other reported techniques.

This paper presents a spike-based model which employs neurons with functionally distinct dendritic compartments for classifying high dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before being linearly integrated at the soma, giving the neuron a capacity to perform a large number of input-output mappings. The model utilizes sparse synaptic connectivity; where each synapse takes a binary value. The optimal connection pattern of a neuron is learned by using a simple hardware-friendly, margin enhancing learning algorithm inspired by the mechanism of structural plasticity in biological neurons. The learning algorithm groups correlated synaptic inputs on the same dendritic branch. Since the learning results in modified connection patterns, it can be incorporated into current event-based neuromorphic systems with little overhead. This work also presents a branch-specific spike-based version of this structural plasticity rule. The proposed model is evaluated on benchmark binary classification problems and its performance is compared against that achieved using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) techniques. Our proposed method attains comparable performance while utilizing 10 to 50% less computational resources than the other reported techniques.

This paper addresses an unconventional application of a production system to a problem involving belief specialization. The production system reduces a large quantity of low-level descriptions into just a few higher-level descriptions that encompass the problem space in a more tractable fashion. This classification process utilizes a set of descriptions generated by combining the component hierarchy of a physical system with the semantics of the terminology employed in its operation. The paper describes an application of this process in a program, constructed in C and CLIPS, that classifies signatures of electromechanical system configurations. The program compares two independent classifications, describing the actual and expected system configurations, in order to generate a set of contradictions between the two.

Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as data augmentation at the training stage. This paper addresses an issue of decision rule for classifiers trained with augmented data. Our method is named as APAC: the Augmented PAttern Classification, which is a way of classification using the optimal decision rule for augmented data learning. Discussion of methods of data augmentation is not our primary focus. We show clear evidences that APAC gives far better generalization performance than the traditional way of class prediction in several experiments. Our convolutional neural network model with APAC achieved a state-of-the-art accuracy on the MNIST dataset among non-ensemble classifiers. Even our multilayer perceptron model beats some of the convolutional models with recently invented stochastic regularization techniques on the CIFAR-10 dataset.

The crosstalk delay associated with global on-chip interconnects becomes more severe in deep submicron technology, and hence can greatly affect the overall system performance. Based on a delay model proposed by Sotiriadis et al., transition patterns over a bus can be classified according to their delays. Using this classification, crosstalk avoidance codes (CACs) have been proposed to alleviate the crosstalk delays by restricting the transition patterns on a bus. In this paper, we first propose a new classification of transition patterns, and then devise a new family of CACs based on this classification. In comparison to the previous classification, our classification has more classes and the delays of its classes do not overlap, both leading to more accurate control of delays. Our new family of CACs includes some previously proposed codes as well as new codes with reduced delays and improved throughput. Thus, this new family of crosstalk avoidance codes provides a wider variety of tradeoffs between bus delay and efficiency. Finally, since our analytical approach to the classification and CACs treats the technology-dependent parameters as variables, our approach can be easily adapted to a wide variety of technology.

Neuromorphic pattern classifiers were implemented, for the first time, using transistor-free integrated crossbar circuits with bilayer metal-oxide memristors. 106- and 108-crosspoint neuromorphic networks were trained in-situ using a Manhattan-Rule algorithm to separate a set of 33 binary images: into 3 classes using the batch-mode training, and into 4 classes using the stochastic-mode training, respectively. Simulation of much larger, multilayer neural network classifiers based on such technology has shown that their fidelity may be on a par with the state-of-the-art results.

In this study we describe the development of a ride assistance application which can be implemented on the widespread smart phones and tablet. The ride assistance application has a signal processing and pattern classification module which yield almost 100% recognition accuracy for real-time signal pattern classification. We introduce a novel framework to build a training dictionary with an overwhelming discriminating capacity which eliminates the need of human intervention spotting the pattern on the training samples. We verify the recognition accuracy of the proposed methodologies by providing the results of another study in which the hand posture and gestures are tracked and recognized for steering a robotic wheelchair.

In this study we describe the development of a ride assistance application which can be implemented on the widespread smart phones and tablet. The ride assistance application has a signal processing and pattern classification module which yield almost 100% recognition accuracy for real-time signal pattern classification. We introduce a novel framework to build a training dictionary with an overwhelming discriminating capacity which eliminates the need of human intervention spotting the pattern on the training samples. We verify the recognition accuracy of the proposed methodologies by providing the results of another study in which the hand posture and gestures are tracked and recognized for steering a robotic wheelchair.

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.

The author attempts a reclassification of structural pattern drills, taking into account the theories of Skinner as well as Chomsky on language learning. Her intent is to propose a "systematic progression in the classroom from mechanical learning to the internalizing of competence." Drills could be used more effeciently in foreign language teaching if they were analyzed in terms of (1) expected terminal behavior, (2) response control, (3) the type of learning process involved, and (4) utterance response. Drills are classified (as mechanical, meaningful, and communicative) and discussed, using illustrations in English and Thai. (AMM)

The missing data in incomplete pattern can have different estimations, and the classification result of pattern with different estimations may be quite distinct. Such uncertainty (ambiguity) of classification is mainly caused by the loss of information in missing data. A new prototype-based credal classification (PCC) method is proposed to classify incomplete patterns using belief functions. The class prototypes obtained by the training data are respectively used to estimate the missing values. Typically, in a c-class problem, one has to deal with c prototypes which yields c estimations. The different edited patterns based on each possible estimation are then classified by a standard classifier and one can get c classification results for an incomplete pattern. Because all these classification results are potentially admissible, they are fused altogether to obtain the credal classification of the incomplete pattern. A new credal combination method is introduced for solving the classification problem, and it is able to characterize the inherent uncertainty due to the possible conflicting results delivered by the different estimations of missing data. The incomplete patterns that are hard to correctly classify will be reasonably committed to some proper meta-classes by PCC method in order to reduce the misclassification rate. The use and potential of PCC method is illustrated through several experiments with artificial and real data sets.

The paper studies for mathematical programming methods which are useful in pattern classification. Two of the models are for linearly separable problems, while the others work without separability.

Infrared spectra of organophosphorus compounds, including pesticides and a set of neurotoxins which have been banned from use by international agreement, along with their precursors and hydrolysis products, were obtained from a variety of sources. The data were treated to minimize spectral information related to the spectral origin. A common spectral wavelength range was selected and spectral data within this range were transduced into data vectors. Computer-assisted classification tools were used to classify the spectra as pesticides versus neurotoxins and their precursors and hydrolysis products. The performance of a k-nearest neighbor classifier for this distinction is compared with several artificial neural network classifiers.

The multi-layer perceptron (MLP) is a type of adaptive layered network often used as a pattern classifier. In more recent literature, MLPs are compared with simpler classification techniques using common datasets. We select two of these simple static pattern classification algorithms and briefly review the relevant techniques. After introducing a modest set of evaluation databases, the performance of the standard classifiers and MLPs are assessed. A technique for implementing the two standard classifiers as MLPs is presented and this novel approach is used to automatically design a 'good' set of initial weights for the MLP networks. Encouraging experimental results for these hybrid techniques are shown for illustration. Keywords: Pattern recognition; Speech; Images; Artificial intelligence.

Some purely chiral fractional quantum Hall states are described by symmetric or anti-symmetric polynomials of infinite variables. In this article, we review a systematic construction and classification of those fractional quantum Hall states and the corresponding polynomials of infinite variables, using the pattern-of-zeros approach. We discuss how to use patterns of zeros to label different fractional quantum Hall states and the corresponding polynomials. We also discuss how to calculate various universal properties (ie the quantum topological invariants) from the pattern of zeros.

Infrared spectroscopy is an important technique for measuring airborne chemicals, for pollution monitoring and to warn of toxic compound releases. Infrared spectroscopy provides both detection and identification of airborne components. Computer-assisted classification tools, including pattern recognition and artificial neural network techniques, have been applied to a collection of infrared spectra of organophosphorus compounds, and these have successfully discriminated commercial pesticide compounds from military nerve agents, precursors, and hydrolysis products. Infrared spectra for previous tests came from a commercial infrared library, with permission, from military laboratories, and from defense contractors. In order to further test such classification tools, additional infrared spectra from the NIST gas-phase infrared library were added to the data set. These additional spectra probed the tendency of the trained classifiers to misidentify unrelated spectra into the trained classes. Infrared spectra used in this effort were gathered from a variety of sources. Different instrument operators collected them at a number of locations, in a variety of spectral data collection designs, and they were delivered in a variety of digital formats. The spectra were treated mathematically to remove artifacts from their collection. Preprocessing techniques used included Fisher weighting and principal component analysis. Classifications were made using the k-nearest neighbor classifier, feed forward neural networks, trained with a variety of techniques, and radial basis function networks. The results from these classification techniques will be reported and compared.

Information systems are often inundated with thousands of attack alerts to distinguish novice hacker probes from genuine threats. Pattern classification can help filter relatively benign attacks from alerts generated by anomaly detectors, limited the numbers of alerts to requiring attention. This research investigates the feasibility of using pattern classification algorithms on network packed header information to classify network attacks. Both liner discrimination and radial basis function algorithms are trained using flood and scan attacks. The classifiers are then tested with unknown floods and scans to determine how well they categorize previously unseen attacks.

http://uf.catalog.fcla.edu/uf.jsp?st=UF000687925&ix=nu&I=0&V=D

Polygraph tests are a widely used method to distinguish between truth, and deception. Polygraph charts are usually analyzed by human interpreters. However, computer algorithms are now being developed to score the tests or verify the results. These methods are based on statistical classification techniques. In this study a number of time, frequency and correlation domain features were selected and used. The fuzzy K-nearest neighbor algorithm was used to classify the polygraph charts, a correct classification of ninety-one percent was obtained for a set of one hundred case files supplied by the NSA.

The use of patterns in predictive models is a topic that has received a lot of attention in recent years. Pattern mining can help to obtain models for structured domains, such as graphs and sequences, and has been proposed as a means to obtain more accurate and more interpretable models. Despite the large amount of publications devoted to this topic, we believe however that an overview of what has been accomplished in this area is missing. This paper presents our perspective on this evolving area. We identify the principles of pattern mining that are important when mining patterns for models and provide an overview of pattern-based classification methods. We categorize these methods along the following dimensions: (1) whether they post-process a pre-computed set of patterns or iteratively execute pattern mining algorithms; (2) whether they select patterns model-independently or whether the pattern selection is guided by a model. We summarize the results that have been obtained for each of these methods.

Preregistration of Hypothesis in the CoScience Project.

The author has identified the following significant results. The sand beach was separated from sea water in each of four bands, if the beach had a width of 100 m or more. Density ranges of the sea for CCT counts were determined as 0-3 for band 7, 0-16 for band 6, 0-25 for band 5, and 0-27 for band 4.

This article is from NeuroImage : Clinical , volume 3 . Abstract Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

The purpose of this study is to introduce a probabilistic approach to classify and diagnose erroneous rules of operation resulting from a variety of misconceptions ('bugs') in a procedural domain of arithmetic. The model contrasts the deterministic approach which has commonly been used in the field of artificial intelligence and shows an advantage in treating the variability of errors in responses. Item response theory (IRT) turned out to be a useful model in integrating the theory of cognitive processes with educational practice. In this paper, erroneous rules of operation in signed-number subtraction problems are represented as points in a geometric space by utilizing IRT. We named this space 'rule space.' This approach seems promising in assessing the state of knowledge as reflected by erroneous rules and in utilizing the information obtained from behaviors of bugs into educational evaluation.

This article is from Indian Journal of Orthopaedics , volume 47 . Abstract Background:: Relapse of clubfoot deformity following correction by Ponseti technique is not uncommon. The relapsed feet progress from flexible to rigid if left untreated and can become as severe as the initial deformity. No definitive classification exists to assess a relapsed clubfoot. Some authors have used the Pirani score to rate the relapse while others have used descriptive terms. The purpose of this study is to analyze the relapse pattern in clubfeet that have undergone treatment with the Ponseti method and propose a simple classification for relapsed clubfeet. Materials and Methods:: Ninety-one children (164 feet) with idiopathic clubfeet who underwent treatment with Ponseti technique presented with relapse of the deformity. There were 68 boys and 23 girls. Mean age at presentation for casting was 10.71 days (range 7-22 days). Seventy three children (146 feet, 80%) had bilateral involvement and 18 (20%) had unilateral clubfeet. The mean Pirani Score was 5.6 and 5.5 in bilateral and unilateral groups respectively. Percutaneous heel cord tenotomy was done in 65 children (130 feet, 89%) in the bilateral group and in 12 children (66%) with unilateral clubfoot. Results:: Five relapse patterns were identified at a mean followup of 4.5 years (range 3-5 years) which forms the basis of this study. These relapse patterns were classified as: Grade IA: decrease in ankle dorsiflexion from15 degrees to neutral, Grade IB: dynamic forefoot adduction or supination, Grade IIA – rigid equinus, Grade IIB – rigid adduction of forefoot/midfoot complex and Grade III: combination of two or more deformities: Fixed equinus, varus and forefoot adduction.In the bilateral group, 21 children (38 feet, 28%) had Grade IA relapse. Twenty four children (46 feet, 34%) had dynamic intoeing (Grade IB) on walking. Thirteen children (22 feet, 16%) had true ankle equinus of varying degress (Grade IIA); eight children (13 feet, 9.7%) had fixed adduction deformity of the forefoot (Grade IIB) and seven children (14 feet, 10.7%) had two or more fixed deformities. In the unilateral group seven cases (38%) had reduced dorsiflexion (Grade IA), six (33%) had dynamic adduction (Grade IB), two (11%) had fixed equinus and adduction respectively (Grade IIA and IIB) and one (5%) child had fixed equinus and adduction deformity (Grade III). The relapses were treated by full time splint application, re-casting, tibialis anterior transfer, posterior release, corrective lateral closing wedge osteotomy and a comprehensive subtalar release. Splint compliance was compromised in both groups. Conclusion:: Relapse pattern in clubfeet can be broadly classified into three distinct subsets. Early identification of relapses and early intervention will prevent major soft tissue surgery. A universal language of relapse pattern will allow comparison of results of intervention.