Издательство InTech, 2009, -430 pp.Machine Learning is often referred to as a branch of artificial intelligence which deals with the design and the development of algorithms and techniques that help machines to learn. Hence, it is closely related to various scientific domains as Optimization, Vision, Robotic and Control, Theoretical Computer Science, etc.
Based on this, Machine Learning can be defined in 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. Machine learning addresses more specifically the ability to improve automatically through experience.
This book brings together many different aspects of the current research on several fields associated to Machine Learning. The selection of the chapters for this book was done in respect to the fact that it comprises a cross-edition of topics that reflect variety of perspectives and disciplinary backgrounds.
Four main parts have been defined and allow gathering the 21 chapters around the following topics: machine learning approaches, Human-like behavior and machine Human interaction, supervised and unsupervised learning approaches, reinforcement learning approaches and their applications.
This book starts with a first set of chapters which addresses general approaches in Machine Learning fields. One can find discussion about various issues: how to use the paradigm divide and conquer to build a hybrid self organized neural network tree structure, how to move from automation to autonomy, how to take experience to a whole new level, how to design very large scale networks based on Hamiltonian neural networks, how to design classifiers generative with similarity based abilities, and how information-theoretic competitive learning can force networks to increase knowledge.
In addition, the second part addresses the problem of Human-like behavior and machine Human interaction. It contains five chapters that deal with the following scope: Human-Knowledge poor-process of ontological information extraction, Machine learning for spoken dialogue system optimization, Bayesian additive regression trees applied to mail phishing detection, Composition of web services under multiple criteria and supervised learning problems under the ranking framework.
Another set of chapters presents an overview and challenges in several areas of supervised and unsupervised learning approaches. Subjects deal with generation method for a person specific facial expression map, linear subspace methods in the context of automatic facial expression analysis, nearest neighbor re-sampling method for prognostic gene expression patterns of tumor patients, 3D shape classification and retrieval algorithm and classification of faults in electrical power systems using a hybrid model based on neural networks.
The last part of the book deals with reinforcement learning approaches used in Machine Learning area. Various techniques are developed: Genetic learning programming and Sarsa learning allow the selection of appropriate stock trading rules in financial area, convergence of the online value-iteration in dynamic programming techniques is given in the case of the optimal control problem for general nonlinear discrete-time systems, modular reinforcement learning with situation-sensitive ability is used for intention estimation, experience replay technique is applied to real-words application and finally sequential modeling and prediction allow an adaptive intrusion detection in computer system.
This book shows that Machine Learning is a very dynamic area in terms of theory and application. The field of Machine Learning has been growing rapidly, producing a wide variety of learning algorithms for different applications. The ultimate value of those algorithms is to a great extent judged by their success in solving real-world problems. There is also a very extensive literature on Machine Learning, and to give a complete bibliography and a historical account of the research that led to the present form would have been impossible. It is thus inevitable that some topics have been treated in less detail than others. The choices made reflect on one hand personal taste and expertise and on second hand a preference for a very promising research and recent developments in Machine Learning fields.Neural Machine Learning Approaches: Q-Learning and Complexity Estimation Based Information Processing System
From Automation To Autonomy
Taking Experience to a Whole New Level
Hamiltonian Neural Networks Based Networks for Learning
Similarity Discriminant Analysis
Forced Information for Information-Theoretic Competitive Learning
Learning to Build a Semantic Thesaurus from Free Text Corpora without External Help
Machine Learning Methods for Spoken Dialogue Simulation and Optimization
Hardening Email Security via Bayesian Additive Regression Trees
Learning Optimal Web Service Selections in Dynamic Environments when Many Quality-of-Service Criteria Matter
Model Selection for Ranking SVM Using Regularization Path
Generation of Facial Expression Map using Supervised and Unsupervised Learning
Linear Subspace Learning for Facial Expression Analysis
Resampling Methods for Unsupervised Learning from Sample Data
3D Shape Classification and Retrieval Using Heterogenous Features and Supervised Learning
Performance Analysis of Hybrid Non-Supervised & Supervised Learning Techniques Applied to the Classification of Faults in Energy Transport Systems
Genetic Network Programming with Reinforcement Learning and Its Application to Creating Stock Trading Rules
Heuristic Dynamic Programming Nonlinear Optimal Controller
Implicit Estimation of Another’s Intention Based on Modular Reinforcement Learning
Machine Learning for Sequential Behavior Modeling and Prediction
