Handbook of pattern recognition and computer vision /
| Други автори: | Chen, C. H. 1937- |
|---|---|
| Формат: | Електронна книга |
| Език: | English |
| Публикувано: |
Hackensack, NJ :
Imperial College Press,
℗♭2010.
|
| Издание: | 4th ed. |
| Предмети: | |
| Онлайн достъп: |
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=340594 |
| Подобни документи: |
Print version::
Handbook of pattern recognition and computer vision. |
Съдържание:
- ""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 MA℗♭nard 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:""