Machine Learning
by Abdelhamid Mellouk, Abdennacer Chebira
Publisher: InTech 2009
ISBN-13: 9789537619561
Number of pages: 450
Description:
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, 3d shape classification and retrieval, genetic network programming with reinforcement learning, heuristic dynamic programming, and more.
Download or read it online for free here:
Download link
(PDF)
Similar books
Boosting: Foundations and Algorithms
by Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
(6552 views)
by Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
(6552 views)
A Brief Introduction to Machine Learning for Engineers
by Osvaldo Simeone - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(6756 views)
by Osvaldo Simeone - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(6756 views)
Machine Learning: A Probabilistic Perspective
by Kevin Patrick Murphy - The MIT Press
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
(3764 views)
by Kevin Patrick Murphy - The MIT Press
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
(3764 views)
A Course in Machine Learning
by Hal Daumé III - ciml.info
Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
(21699 views)
by Hal Daumé III - ciml.info
Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
(21699 views)