Machine Learning and Data Mining: Methods and Applications / Edition 1

Machine Learning and Data Mining: Methods and Applications / Edition 1

by Ryszad S. Michalski, Ivan Bratko, Miroslav Kubat
     
 

ISBN-10: 0471971995

ISBN-13: 9780471971993

Pub. Date: 08/14/1998

Publisher: Wiley

Master the new computational tools to get the most out of your information system.

This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data.  See more details below

Overview

Master the new computational tools to get the most out of your information system.

This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data.

Product Details

ISBN-13:
9780471971993
Publisher:
Wiley
Publication date:
08/14/1998
Edition description:
New Edition
Pages:
472
Product dimensions:
1.13(w) x 9.21(h) x 6.14(d)

Table of Contents

GENERAL TOPICS.

A Review of Machine Learning Methods (M. Kubat, et al.).

Data Mining and Knowledge Discovery: A Review of Issues and Multistrategy Approach (R. Michalski & K. Kaufman).

Fielded Applications of Machine Learning (P. Langley & H. Simon).

Applications of Inductive Logic Programming (I. Bratko, et al.).

DESIGN AND ENGINEERING.

Application of Machine Learning in Finite Element Computation (B. Dolsak, et al.).

Application of Inductive Learning and Case-Based Reasoning for Troubleshooting Industrial Machines (M. Manago & E. Auriol).

Empirical Assembly Sequence Planning: A Multistrategy Constructive learning Approach (H. Ko).

Inductive Learning in Design: A Method and Case Study Concerning Design of Antifriction Bearing Systems (W. Moczulski).

DETECTION OF PATTERNS IN TEXTS, IMAGES AND MUSIC.

Finding Associations in Collections of Text (R. Feldman & H. Hirsh).

Learning Patterns in Images (R. Michalski, et al.).

Applications of Machine Learning to Music Research: Empirical Investigations into the Phenomenon of Musical Expression (G. Widmer).

COMPUTER SYSTEMS AND CONTROL SYSTEMS.

WebWatcher: A Learning Apprentice for the World Wide Web (R. Armstrong, et al.).

Biologically Inspired Defences Against Computer Viruses (J. Kephart, et al.).

Behavioural Cloning of Control Skill (I. Bratko, et al.).

Acquiring First-order Knowledge About Air Traffic Control (Y. Kodratoff & C. Vrain).

MEDICINE AND BIOLOGY.

Application of Machine Learning to Medical Diagnosis (I. Kononenko, et al.).

Learning to Classify Biomedical Signals (M. Kubat, et al.).

Machine Learning Applications in Biological Classification of River Water Quality (S. Deroski, et al.).

Index.

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