Feature Extraction

Table of Contents

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   Book Overview
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   Chapter 1 An Introduction to Feature Extraction
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   Chapter 2 Learning Machines
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   Chapter 3 Assessment Methods
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   Chapter 4 Filter Methods
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   Chapter 5 Search Strategies
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   Chapter 6 Embedded Methods
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   Chapter 7 Information-Theoretic Methods
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   Chapter 8 Ensemble Learning
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    Chapter 9 Fuzzy Neural Networks
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    Chapter 10 Design and Analysis of the NIPS2003 Challenge
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    Chapter 11 High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees
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    Chapter 12 Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems
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    Chapter 13 Combining SVMs with Various Feature Selection Strategies
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    Chapter 14 Feature Selection with Transductive Support Vector Machines
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    Chapter 15 Variable Selection using Correlation and Single Variable Classifier Methods: Applications
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    Chapter 16 Tree-Based Ensembles with Dynamic Soft Feature Selection
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    Chapter 17 Sparse, Flexible and Efficient Modeling using L 1 Regularization
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    Chapter 18 Margin Based Feature Selection and Infogain with Standard Classifiers
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    Chapter 19 Bayesian Support Vector Machines for Feature Ranking and Selection
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    Chapter 20 Nonlinear Feature Selection with the Potential Support Vector Machine
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    Chapter 21 Combining a Filter Method with SVMs
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    Chapter 22 Feature Selection via Sensitivity Analysis with Direct Kernel PLS
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    Chapter 23 Information Gain, Correlation and Support Vector Machines
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    Chapter 24 Mining for Complex Models Comprising Feature Selection and Classification
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    Chapter 25 Combining Information-Based Supervised and Unsupervised Feature Selection
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    Chapter 26 An Enhanced Selective Naïve Bayes Method with Optimal Discretization
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    Chapter 27 An Input Variable Importance Definition based on Empirical Data Probability Distribution
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    Chapter 28 Spectral Dimensionality Reduction
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    Chapter 29 Constructing Orthogonal Latent Features for Arbitrary Loss
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    Chapter 30 Large Margin Principles for Feature Selection
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    Chapter 31 Feature Extraction for Classification of Proteomic Mass Spectra: A Comparative Study
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    Chapter 32 Sequence Motifs: Highly Predictive Features of Protein Function