Amazon cover image
Image from Amazon.com

Handbook of pattern recognition and computer vision / editor, C.H. Chen.

Contributor(s): Chen, C. H. (Chi-hau), 1937-Material type: TextTextPublication details: Hackensack, NJ : Imperial College Press, ©2010. Edition: 4th edDescription: 1 online resource (xx, 776 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9789814273398; 9814273392Subject(s): Pattern recognition systems | Computer vision | COMPUTERS -- Optical Data Processing | Computer vision | Pattern recognition systemsGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Handbook of pattern recognition and computer vision.DDC classification: 006.4 LOC classification: TK7882.P3 | H35 2010ebOnline resources: Click here to access online
Contents:
""A Brief Introduction to the Handbook Series (by C.H. Chen)""; ""Preface to the 4th Edition (by C.H. Chen)""; ""Contents""; ""Part 1. Basic Methods in Pattern Recognition""; ""Chapter 1.1 A Unification of Component Analysis Methods F. De la Torre""; ""1. Introduction""; ""2. CovarianceMatrices in Component Analysis""; ""3. A GenerativeModel for Component Analysis""; ""3.1. Least-Squares Weighted Kernel Reduced Rank Regression (LS-WKRRR)""; ""3.2. Computational Aspects of LS-WKRRR""; ""3.2.1. Subspace Iteration""; ""3.2.2. Alternated Least Squares (ALS)""
""4. PCA, KPCA, and Weighted Extensions""""4.1. Principal Component Analysis (PCA)""; ""4.2. Kernel Principal Component Analysis (KPCA)""; ""4.3. Weighted Extensions""; ""5. LDA, KLDA, CCA, KCCA and Weighted Extensions""; ""5.1. Linear Discriminant Analysis (LDA)""; ""5.2. Kernel Linear Discriminant Analysis (KLDA)""; ""5.3. Canonical Correlation Analysis (CCA) and Kernel CCA""; ""5.4. Weighted Extensions""; ""6. K-means and Spectral Clustering""; ""6.1. k-means""; ""6.2. Normalized Cuts""; ""7. Conclusions""; ""Acknowledgment""; ""References""
""Chapter 1.2 Multiple Classifier Systems: Tools and Methods Veyis Gunes, Michael Ménard and Simon Petitrenaud""""1. Introduction""; ""2. Advantages of Multiple Classifier Systems""; ""3. Taxonomy of Multiple Classifier Systems""; ""4. Combination of Classifiers""; ""4.1. Combinations inspired by the voting methods (output types: 1,2,3)""; ""4.2. Some usual and useful combinations (output type: 3)""; ""4.3. Combination by the probability theory (output type: 3)""; ""4.4. Combination by the Belief theory (output type: 3)""; ""4.5. Other theoretical frameworks for the combination""
""5. Cooperation of Classifiers""""6. Selection of Classifiers""; ""7. Hybrid Methods in Multiple Classifier Systems""; ""8. Conclusion""; ""References""; ""Chapter 1.3 On Dissimilarity Embedding of Graphs in Vector Spaces Horst Bunke and Kaspar Riesen""; ""1. Introduction""; ""2. Basic Concepts and Notation""; ""2.1. Graph Based Pattern Representation""; ""2.2. Graph Edit Distance""; ""3. Graph Embedding by Means of Dissimilarity Representation""; ""3.1. General Embedding Procedure and Properties""; ""3.2. Relation to Kernel Methods""; ""3.3. The Problem of Prototype Selection""
""4. Prototype Selection Strategies""""5. Experimental Evaluation""; ""5.1. Graph Data Sets""; ""5.2. Reference Systems and Experimental Setup""; ""5.3. Results and Discussion""; ""6. Conclusions""; ""References""; ""Chapter 1.4 Match Tracking Strategies for Fuzzy ARTMAP Neural Networks Eric Granger, Philippe Henniges, Robert Sabourin and Luiz S. Oliveira""; ""1. Introduction""; ""2. Fuzzy ARTMAP Match Tracking""; ""2.1. The fuzzy ARTMAP neural network:""; ""2.2. Algorithm for supervised learning of fuzzy ARTMAP:""; ""2.3. Match tracking strategies:""
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library

Electronic Book@IST

EBook Available
Total holds: 0

Includes bibliographical references and index.

Print version record.

""A Brief Introduction to the Handbook Series (by C.H. Chen)""; ""Preface to the 4th Edition (by C.H. Chen)""; ""Contents""; ""Part 1. Basic Methods in Pattern Recognition""; ""Chapter 1.1 A Unification of Component Analysis Methods F. De la Torre""; ""1. Introduction""; ""2. CovarianceMatrices in Component Analysis""; ""3. A GenerativeModel for Component Analysis""; ""3.1. Least-Squares Weighted Kernel Reduced Rank Regression (LS-WKRRR)""; ""3.2. Computational Aspects of LS-WKRRR""; ""3.2.1. Subspace Iteration""; ""3.2.2. Alternated Least Squares (ALS)""

""4. PCA, KPCA, and Weighted Extensions""""4.1. Principal Component Analysis (PCA)""; ""4.2. Kernel Principal Component Analysis (KPCA)""; ""4.3. Weighted Extensions""; ""5. LDA, KLDA, CCA, KCCA and Weighted Extensions""; ""5.1. Linear Discriminant Analysis (LDA)""; ""5.2. Kernel Linear Discriminant Analysis (KLDA)""; ""5.3. Canonical Correlation Analysis (CCA) and Kernel CCA""; ""5.4. Weighted Extensions""; ""6. K-means and Spectral Clustering""; ""6.1. k-means""; ""6.2. Normalized Cuts""; ""7. Conclusions""; ""Acknowledgment""; ""References""

""Chapter 1.2 Multiple Classifier Systems: Tools and Methods Veyis Gunes, Michael Ménard and Simon Petitrenaud""""1. Introduction""; ""2. Advantages of Multiple Classifier Systems""; ""3. Taxonomy of Multiple Classifier Systems""; ""4. Combination of Classifiers""; ""4.1. Combinations inspired by the voting methods (output types: 1,2,3)""; ""4.2. Some usual and useful combinations (output type: 3)""; ""4.3. Combination by the probability theory (output type: 3)""; ""4.4. Combination by the Belief theory (output type: 3)""; ""4.5. Other theoretical frameworks for the combination""

""5. Cooperation of Classifiers""""6. Selection of Classifiers""; ""7. Hybrid Methods in Multiple Classifier Systems""; ""8. Conclusion""; ""References""; ""Chapter 1.3 On Dissimilarity Embedding of Graphs in Vector Spaces Horst Bunke and Kaspar Riesen""; ""1. Introduction""; ""2. Basic Concepts and Notation""; ""2.1. Graph Based Pattern Representation""; ""2.2. Graph Edit Distance""; ""3. Graph Embedding by Means of Dissimilarity Representation""; ""3.1. General Embedding Procedure and Properties""; ""3.2. Relation to Kernel Methods""; ""3.3. The Problem of Prototype Selection""

""4. Prototype Selection Strategies""""5. Experimental Evaluation""; ""5.1. Graph Data Sets""; ""5.2. Reference Systems and Experimental Setup""; ""5.3. Results and Discussion""; ""6. Conclusions""; ""References""; ""Chapter 1.4 Match Tracking Strategies for Fuzzy ARTMAP Neural Networks Eric Granger, Philippe Henniges, Robert Sabourin and Luiz S. Oliveira""; ""1. Introduction""; ""2. Fuzzy ARTMAP Match Tracking""; ""2.1. The fuzzy ARTMAP neural network:""; ""2.2. Algorithm for supervised learning of fuzzy ARTMAP:""; ""2.3. Match tracking strategies:""

Powered by Koha