On the state of the art in machine learning: A personal review

被引：35

作者：

Flach, PA ^{[1
]}

机构：

[1] Univ Bristol, Dept Comp Sci, Bristol BS8 1UB, Avon, England

来源：

ARTIFICIAL INTELLIGENCE | 2001年 / 131卷 / 1-2期

关键词：

machine learning; data mining; support vector machines; graphical probabilistic models;

D O I：

10.1016/S0004-3702(01)00125-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper reviews a number of recent books related to current developments in machine learning. Some (anticipated) trends will be sketched. These include: a trend towards combining approaches that were hitherto regarded as distinct and were studied by separate research communities; a trend towards a more prominent role of representation; and a tighter integration of machine teaming techniques with techniques from areas of application such as bioinformatics. The intended readership has some knowledge of what machine learning is about, but brief tutorial introductions to some of the more specialist research areas will also be given. (C) 2001 Elsevier Science B.V All rights reserved.

引用

页码：199 / 222

页数：24

共 21 条

[1]

[Anonymous], GRAPHICAL MODELS MAC

[2]

[Anonymous], P 5 ANN WORKSH COMP

[3]

Baldi P., 1998, Bioinformatics: The machine learning approach

[4]

Berry MichaelJ., 1997, DATA MINING TECHNIQU

[5]

Cristianini N, 2000, Intelligent Data Analysis: An Introduction

[6] Bucket elimination: A unifying framework for reasoning [J].

Dechter, R .

ARTIFICIAL INTELLIGENCE, 1999, 113 (1-2) :41-85

[7]

Durbin R., 1998, BIOL SEQUENCE ANAL P

[8]

Fielding A., 1999, MACHINE LEARNING MET

[9]

Glymour C., 1999, Computation, Causation, and Discovery

[10]

Jordan MI., 1999, Learning in graphical models

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