Statistical pattern recognition.

"Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book describes techniques for analysing data com...

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Bibliographic Details
Main Author:	Webb, A. R. (Andrew R.)
Other Authors:	Copsey, Keith D., Cawley, Gavin
Format:	eBook
Language:	English
Published:	Oxford : Wiley-Blackwell, 2011.
Edition:	3rd ed. /
Subjects:	Pattern perception > Statistical methods. Decision making > Mathematical models. Mathematical statistics. COMPUTERS > Optical Data Processing. Electronic books.
Online Access:	Full Text via HEAL-Link

Table of Contents:

Note continued: 9.3.Comparing Classifier Performance
9.3.1. Which Technique is Best?
9.3.2. Statistical Tests
9.3.3.Comparing Rules When Misclassification Costs are Uncertain
9.3.4. Example Application Study
9.3.5. Further Developments
9.3.6. Summary
9.4. Application Studies
9.5. Summary and Discussion
9.6. Recommendations
9.7. Notes and References
Exercises
10. Feature Selection and Extraction
10.1. Introduction
10.2. Feature Selection
10.2.1. Introduction
10.2.2. Characterisation of Feature Selection Approaches
10.2.3. Evaluation Measures
10.2.4. Search Algorithms for Feature Subset Selection
10.2.5.Complete Search
Branch and Bound
10.2.6. Sequential Search
10.2.7. Random Search
10.2.8. Markov Blanket
10.2.9. Stability of Feature Selection
10.2.10. Example Application Study
10.2.11. Further Developments
10.2.12. Summary
10.3. Linear Feature Extraction
10.3.1. Principal Components Analysis
10.3.2. Karhunen-Loeve Transformation
10.3.3. Example Application Study
10.3.4. Further Developments
10.3.5. Summary
10.4. Multidimensional Scaling
10.4.1. Classical Scaling
10.4.2. Metric MDS
10.4.3. Ordinal Scaling
10.4.4. Algorithms
10.4.5. MDS for Feature Extraction
10.4.6. Example Application Study
10.4.7. Further Developments
10.4.8. Summary
10.5. Application Studies
10.6. Summary and Discussion
10.7. Recommendations
10.8. Notes and References
Exercises
11. Clustering
11.1. Introduction
11.2. Hierarchical Methods
11.2.1. Single-Link Method
11.2.2.Complete-Link Method
11.2.3. Sum-of-Squares Method
11.2.4. General Agglomerative Algorithm
11.2.5. Properties of a Hierarchical Classification
11.2.6. Example Application Study
11.2.7. Summary
11.3. Quick Partitions
11.4. Mixture Models
11.4.1. Model Description
11.4.2. Example Application Study
11.5. Sum-of-Squares Methods
11.5.1. Clustering Criteria
11.5.2. Clustering Algorithms
11.5.3. Vector Quantisation
11.5.4. Example Application Study
11.5.5. Further Developments
11.5.6. Summary
11.6. Spectral Clustering
11.6.1. Elementary Graph Theory
11.6.2. Similarity Matrices
11.6.3. Application to Clustering
11.6.4. Spectral Clustering Algorithm
11.6.5. Forms of Graph Laplacian
11.6.6. Example Application Study
11.6.7. Further Developments
11.6.8. Summary
11.7. Cluster Validity
11.7.1. Introduction
11.7.2. Statistical Tests
11.7.3. Absence of Class Structure
11.7.4. Validity of Individual Clusters
11.7.5. Hierarchical Clustering
11.7.6. Validation of Individual Clusterings
11.7.7. Partitions
11.7.8. Relative Criteria
11.7.9. Choosing the Number of Clusters
11.8. Application Studies
11.9. Summary and Discussion
11.10. Recommendations
11.11. Notes and References
Exercises
12.Complex Networks
12.1. Introduction
12.1.1. Characteristics
12.1.2. Properties
12.1.3. Questions to Address
12.1.4. Descriptive Features
12.1.5. Outline
12.2. Mathematics of Networks
12.2.1. Graph Matrices
12.2.2. Connectivity
12.2.3. Distance Measures
12.2.4. Weighted Networks
12.2.5. Centrality Measures
12.2.6. Random Graphs
12.3.Community Detection
12.3.1. Clustering Methods
12.3.2. Girvan-Newman Algorithm
12.3.3. Modularity Approaches
12.3.4. Local Modularity
12.3.5. Clique Percolation
12.3.6. Example Application Study
12.3.7. Further Developments
12.3.8. Summary
12.4. Link Prediction
12.4.1. Approaches to Link Prediction
12.4.2. Example Application Study
12.4.3. Further Developments
12.5. Application Studies
12.6. Summary and Discussion
12.7. Recommendations
12.8. Notes and References
Exercises
13. Additional Topics
13.1. Model Selection
13.1.1. Separate Training and Test Sets
13.1.2. Cross-Validation
13.1.3. The Bayesian Viewpoint
13.1.4. Akaike's Information Criterion
13.1.5. Minimum Description Length
13.2. Missing Data
13.3. Outlier Detection and Robust Procedures
13.4. Mixed Continuous and Discrete Variables
13.5. Structural Risk Minimisation and the Vapnik-Chervonenkis Dimension
13.5.1. Bounds on the Expected Risk
13.5.2. The VC Dimension.

Statistical pattern recognition.

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