期刊缩写       MACH LEARN

期刊全称       MACHINE LEARNING 机器学习

期刊ISSN      0885-6125

2013-2014最新影响因子     1.689

期刊官方网站       http://link.springer.com/journal/10994

期刊投稿网址       https://www.editorialmanager.com/mach/

通讯方式       SPRINGER, VAN GODEWIJCKSTRAAT 30, DORDRECHT, NETHERLANDS, 3311 GZ

涉及的研究方向    工程技术-计算机：人工智能

出版国家       UNITED STATES

出版周期       Monthly

出版年份       1986

年文章数       57

 

 

Editor-in-Chief: Peter A. Flach

ISSN: 0885-6125 (print version)

ISSN: 1573-0565 (electronic version)

Journal no. 10994

 

...THE CUTTING EDGE IN AI RESEARCH....

An international forum for research on computational approaches to learning.

Reports substantive results on a wide range of learning methods applied to a variety of learning problems.

Provides solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena.

Shows how to apply learning methods to solve important applications problems.

Improves how machine learning research is conducted.

Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems.

 

The journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted.

 

All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.

 

 

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

 

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

 

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.