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Neural Networks and Statistical Learning

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Cover of 'Neural Networks and Statistical Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Fundamentals of Machine Learning
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    Chapter 3 Elements of Computational Learning Theory
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    Chapter 4 Perceptrons
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    Chapter 5 Multilayer Perceptrons: Architecture and Error Backpropagation
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    Chapter 6 Multilayer Perceptrons: Other Learing Techniques
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    Chapter 7 Hopfield Networks, Simulated Annealing, and Chaotic Neural Networks
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    Chapter 8 Associative Memory Networks
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    Chapter 9 Clustering I: Basic Clustering Models and Algorithms
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    Chapter 10 Clustering II: Topics in Clustering
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    Chapter 11 Radial Basis Function Networks
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    Chapter 12 Recurrent Neural Networks
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    Chapter 13 Principal Component Analysis
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    Chapter 14 Nonnegative Matrix Factorization
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    Chapter 15 Independent Component Analysis
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    Chapter 16 Discriminant Analysis
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    Chapter 17 Reinforcement Learning
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    Chapter 18 Compressed Sensing and Dictionary Learning
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    Chapter 19 Matrix Completion
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    Chapter 20 Kernel Methods
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    Chapter 21 Support Vector Machines
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    Chapter 22 Probabilistic and Bayesian Networks
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    Chapter 23 Boltzmann Machines
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    Chapter 24 Deep Learning
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    Chapter 25 Combining Multiple Learners: Data Fusion and Ensemble Learning
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    Chapter 26 Introduction to Fuzzy Sets and Logic
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    Chapter 27 Neurofuzzy Systems
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    Chapter 28 Neural Network Circuits and Parallel Implementations
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    Chapter 29 Pattern Recognition for Biometrics and Bioinformatics
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    Chapter 30 Data Mining
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    Chapter 31 Big Data, Cloud Computing, and Internet of Things
Attention for Chapter 9: Clustering I: Basic Clustering Models and Algorithms
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Citations

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