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Structural Pattern Recognition. by Theo Pavlidis

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1Schalkoff1992 - Pattern Recognition Statistical, Structural, And Neural Approaches

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patternrecogniti0000robe OpenLibrary:- OL3353071W

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  • Title: ➤  Schalkoff1992 - Pattern Recognition Statistical, Structural, And Neural Approaches
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 321.73 Mbs, the file-s went public at Thu Jul 24 2025.

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2Introduction To Pattern Recognition : Statistical, Structural, Neural, And Fuzzy Logic Approaches

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patternrecogniti0000robe OpenLibrary:- OL3353071W

“Introduction To Pattern Recognition : Statistical, Structural, Neural, And Fuzzy Logic Approaches” Metadata:

  • Title: ➤  Introduction To Pattern Recognition : Statistical, Structural, Neural, And Fuzzy Logic Approaches
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  • Language: English

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3Structural Methods In Pattern Recognition

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patternrecogniti0000robe OpenLibrary:- OL3353071W

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  • Title: ➤  Structural Methods In Pattern Recognition
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 326.48 Mbs, the file-s for this book were downloaded 37 times, the file-s went public at Fri Nov 01 2019.

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4Structural Pattern Recognition

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patternrecogniti0000robe OpenLibrary:- OL3353071W

“Structural Pattern Recognition” Metadata:

  • Title: Structural Pattern Recognition
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  • Language: English

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5Syntactic And Structural Pattern Recognition

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patternrecogniti0000robe OpenLibrary:- OL3353071W

“Syntactic And Structural Pattern Recognition” Metadata:

  • Title: ➤  Syntactic And Structural Pattern Recognition
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 1108.78 Mbs, the file-s for this book were downloaded 35 times, the file-s went public at Mon May 23 2022.

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6Advances In Structural And Syntactical Pattern Recognition : 6th International Workshop, SSPR '96, Leipzig, Germany, August 20-23, 1996, Proceedings

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patternrecogniti0000robe OpenLibrary:- OL3353071W

“Advances In Structural And Syntactical Pattern Recognition : 6th International Workshop, SSPR '96, Leipzig, Germany, August 20-23, 1996, Proceedings” Metadata:

  • Title: ➤  Advances In Structural And Syntactical Pattern Recognition : 6th International Workshop, SSPR '96, Leipzig, Germany, August 20-23, 1996, Proceedings
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  • Language: English

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7Structural, Syntactic, And Statistical Pattern Recognition : Joint IAPR International Workshops SSPR 2002 And SPR 2002, Windsor, Ontario, Canada, August 6-9, 2002 : Proceedings

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patternrecogniti0000robe OpenLibrary:- OL3353071W

“Structural, Syntactic, And Statistical Pattern Recognition : Joint IAPR International Workshops SSPR 2002 And SPR 2002, Windsor, Ontario, Canada, August 6-9, 2002 : Proceedings” Metadata:

  • Title: ➤  Structural, Syntactic, And Statistical Pattern Recognition : Joint IAPR International Workshops SSPR 2002 And SPR 2002, Windsor, Ontario, Canada, August 6-9, 2002 : Proceedings
  • Author: ➤  
  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 1721.00 Mbs, the file-s for this book were downloaded 5 times, the file-s went public at Fri Sep 08 2023.

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8DTIC ADA457624: Generalized Feature Extraction For Structural Pattern Recognition In Time-Series Data

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Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pattern) and then classifies the object based on that description (i.e., the recognition). Two general approaches for implementing pattern recognition systems, statistical and structural, employ different techniques for description and classification. Statistical approaches to pattern recognition use decision-theoretic concepts to discriminate among objects belonging to different groups based upon their quantitative features. Structural approaches to pattern recognition use syntactic grammars to discriminate among objects belonging to different groups based upon the arrangement of their morphological features. Hybrid approaches to pattern recognition combine aspects of both statistical and structural pattern recognition. Structural pattern recognition systems are difficult to apply to new domains because implementation of both the description and classification tasks requires domain knowledge. Knowledge acquisition techniques necessary to obtain domain knowledge from experts are tedious and often fail to produce a complete and accurate knowledge base. Consequently, applications of structural pattern recognition have been primarily restricted to domains in which the set of useful morphological features has been established in the literature and the syntactic grammars can be composed by hand (e.g., electrocardiogram diagnosis). To overcome this limitation, a domain-independent approach to structural pattern recognition is needed that is capable of extracting morphological features and performing classification without relying on domain knowledge. This thesis presents a suite of structure detectors that effectively performs generalized feature extraction for structural pattern recognition in time-series data.

“DTIC ADA457624: Generalized Feature Extraction For Structural Pattern Recognition In Time-Series Data” Metadata:

  • Title: ➤  DTIC ADA457624: Generalized Feature Extraction For Structural Pattern Recognition In Time-Series Data
  • Author: ➤  
  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 89.13 Mbs, the file-s for this book were downloaded 72 times, the file-s went public at Thu Jun 07 2018.

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9Syntactic And Structural Pattern Recognition : Theory And Applications

Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pattern) and then classifies the object based on that description (i.e., the recognition). Two general approaches for implementing pattern recognition systems, statistical and structural, employ different techniques for description and classification. Statistical approaches to pattern recognition use decision-theoretic concepts to discriminate among objects belonging to different groups based upon their quantitative features. Structural approaches to pattern recognition use syntactic grammars to discriminate among objects belonging to different groups based upon the arrangement of their morphological features. Hybrid approaches to pattern recognition combine aspects of both statistical and structural pattern recognition. Structural pattern recognition systems are difficult to apply to new domains because implementation of both the description and classification tasks requires domain knowledge. Knowledge acquisition techniques necessary to obtain domain knowledge from experts are tedious and often fail to produce a complete and accurate knowledge base. Consequently, applications of structural pattern recognition have been primarily restricted to domains in which the set of useful morphological features has been established in the literature and the syntactic grammars can be composed by hand (e.g., electrocardiogram diagnosis). To overcome this limitation, a domain-independent approach to structural pattern recognition is needed that is capable of extracting morphological features and performing classification without relying on domain knowledge. This thesis presents a suite of structure detectors that effectively performs generalized feature extraction for structural pattern recognition in time-series data.

“Syntactic And Structural Pattern Recognition : Theory And Applications” Metadata:

  • Title: ➤  Syntactic And Structural Pattern Recognition : Theory And Applications
  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 1269.49 Mbs, the file-s for this book were downloaded 67 times, the file-s went public at Wed Jan 13 2021.

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1Structural pattern recognition

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“Structural pattern recognition” Metadata:

  • Title: Structural pattern recognition
  • Author:
  • Language: ger
  • Number of Pages: Median: 302
  • Publisher: Springer-Verlag
  • Publish Date:
  • Publish Location: Berlin

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Access and General Info:

  • First Year Published: 1977
  • Is Full Text Available: Yes
  • Is The Book Public: No
  • Access Status: Borrowable

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