"Machine Learning" ed. by Abdelhamid Mellouk and Abdennacer Chebira

This book is about various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior.

The book brings together many different aspects of the current research on several fields associated to Machine Learning. This book shows that Machine Learning is a very dynamic area in terms of theory and application.

Contents
Preface
1 Neural Machine Learning Approaches: Q-Learning and Complexity Estimation Based Information Processing System
2 From Automation To Autonomy
3 Taking Experience to a Whole New Level
4 Hamiltonian Neural Networks Based Networks for Learning
5 Similarity Discriminant Analysis
6 Forced Information for Information-Theoretic Competitive Learning
7 Learning to Build a Semantic Thesaurus from Free Text Corpora without External Help
8 Machine Learning Methods for Spoken Dialogue Simulation and Optimization
9 Hardening Email Security via Bayesian Additive Regression Trees
10 Learning Optimal Web Service Selections in Dynamic Environments when Many Quality-of-Service Criteria Matter
11 Model Selection for Ranking SVM Using Regularization Path
12 Generation of Facial Expression Map using Supervised and Unsupervised Learning
13 Linear Subspace Learning for Facial Expression Analysis
14 Resampling Methods for Unsupervised Learning from Sample Data
15 3D Shape Classification and Retrieval Using Heterogenous Features and Supervised Learning
16 Performance Analysis of Hybrid Non-Supervised & Supervised Learning Techniques Applied to the Classification of Faults in Energy Transport Systems
17 Genetic Network Programming with Reinforcement Learning and Its Application to Creating Stock Trading Rules
18 Heuristic Dynamic Programming Nonlinear Optimal Controller
19 Implicit Estimation of Another’s Intention Based on Modular Reinforcement Learning
20 Machine Learning for Sequential Behavior Modeling and Prediction