Progress in Artificial Intelligence and Pattern Recognition - Info and Reading Options

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Intro -- Preface -- Organization -- Contents -- Keynote Lecture -- Improving Explanatory Power of Machine Learning in the Symbolic Data Analysis Framework -- Abstract -- 1 Introduction -- 2 Building Symbolic Data from Given Classes or Clusters -- 3 Explanatory Power of Classes or Clusters from Their Associated Symbolic Data Table -- 4 Improving Explanatory Power of Machine Learning by Using a Filter -- 5 Conclusion -- References -- Artificial Intelligence and Applications -- Classification of Neuron Sets from Non-disease States Using Time Series Obtained Through Nonlinear Analysis of the 3D Dendritic Structures -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Data Bases -- 2.2 Time Series Representation of Neurons Trees -- 2.3 Nonlinear Dynamic Test -- 2.4 Obtaining Features from Time Series -- 2.5 Classification Process -- 3 Results -- 3.1 Recursive Classification Process -- 3.2 Chaos Test -- 3.3 Nares Occlusion Set -- 3.4 siRNA-KD Set Results -- 4 Discussion -- 4.1 Morphological Features Vs. Time Series Features -- 4.2 Sholl Analysis, Morphological and Time Series Metrics -- 5 Conclusion -- References -- Evaluating the Max-Min Hill-Climbing Estimation of Distribution Algorithm on B-Functions -- Abstract -- 1 Introduction -- 2 The Max-Min Hill-Climbing Algorithm -- 2.1 An EDA Based on the MMHC Algorithm -- 3 Numerical Results -- 3.1 B-Functions -- 3.2 MMHCEDA Scales with the Function FirstPolytree5 -- 3.3 Minimization of BF2B30s4-2312 and BF2B30s4-1245 -- 4 Conclusions and Future Work -- References -- Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets -- 1 Introduction -- 2 Methodology -- 2.1 CNN Classification of IVUS Pullbacks -- 2.2 Sequence-Based Analysis (Smoothing and Thresholding) -- 3 Materials, Experiments and Results -- 3.1 Materials -- 3.2 Experiments -- 3.3 Results -- 4 Conclusions

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