Machine Learning Proceedings 1989 PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Machine Learning Proceedings 1989 PDF full book. Access full book title Machine Learning Proceedings 1989 by Alberto Maria Segre. Download full books in PDF and EPUB format.
Author: Alberto Maria Segre
Publisher: Morgan Kaufmann
ISBN: 1483297403
Category : Computers
Languages : en
Pages : 521
Get Book Here
Book Description
Machine Learning Proceedings 1989
Author: Alberto Maria Segre
Publisher: Morgan Kaufmann
ISBN: 1483297403
Category : Computers
Languages : en
Pages : 521
Get Book Here
Book Description
Machine Learning Proceedings 1989
Author: Peter Edwards
Publisher: Morgan Kaufmann
ISBN: 1483298531
Category : Computers
Languages : en
Pages : 497
Get Book Here
Book Description
Machine Learning Proceedings 1992
Author: J. Ross Quinlan
Publisher: Morgan Kaufmann
ISBN: 9781558602380
Category : Computers
Languages : en
Pages : 286
Get Book Here
Book Description
This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes.
Author: Lawrence A. Birnbaum
Publisher: Morgan Kaufmann
ISBN: 1483298175
Category : Computers
Languages : en
Pages : 682
Get Book Here
Book Description
Machine Learning
Author: Armand Prieditis
Publisher: Morgan Kaufmann
ISBN: 1483298663
Category : Computers
Languages : en
Pages : 606
Get Book Here
Book Description
Machine Learning Proceedings 1995
Author: Yves Kodratoff
Publisher: Morgan Kaufmann
ISBN: 9781558601192
Category : Computers
Languages : en
Pages : 840
Get Book Here
Book Description
One of the largest and most active areas of AI, machine learning is of interest to students of psychology, philosophy of science, and education. Although self-contained, volume III follows the tradition of volume I (1983) and volume II (1986). Annotation copyrighted by Book News, Inc., Portland, OR
Author: William W. Cohen
Publisher: Morgan Kaufmann
ISBN: 1483298183
Category : Computers
Languages : en
Pages : 398
Get Book Here
Book Description
Machine Learning Proceedings 1994
Author: Philippe G. LeFloch
Publisher: Springer Science & Business Media
ISBN: 9783764366872
Category : Mathematics
Languages : en
Pages : 1010
Get Book Here
Book Description
This book examines the well-posedness theory for nonlinear hyperbolic systems of conservation laws, recently completed by the author together with his collaborators. It covers the existence, uniqueness, and continuous dependence of classical entropy solutions. It also introduces the reader to the developing theory of nonclassical (undercompressive) entropy solutions. The systems of partial differential equations under consideration arise in many areas of continuum physics.
Author: John J. Grefenstette
Publisher: Springer Science & Business Media
ISBN: 1461527406
Category : Computers
Languages : en
Pages : 167
Get Book Here
Book Description
The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.
Author: Jude W. Shavlik
Publisher: Morgan Kaufmann
ISBN: 9781558601437
Category : Computers
Languages : en
Pages : 868
Get Book Here
Book Description
The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.
