Machine Learning and Data Mining: Methods and Applications / Edition 1
by Ryszad S. Michalski, Ivan Bratko, Miroslav Kubat
Being able to extract meaningful patterns and strategic knowledge from large stores of information held electronically is increasingly a challenge facing the business and science worlds. In response, methods are being developed for discovering patterns and general rules from the information in databases, data warehouses and document information systems. This book
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Being able to extract meaningful patterns and strategic knowledge from large stores of information held electronically is increasingly a challenge facing the business and science worlds. In response, methods are being developed for discovering patterns and general rules from the information in databases, data warehouses and document information systems. This book is the first major text dedicated to issues at the intersection of machine learning and data mining - two interrelated fields that provide the foundations for these methods. Written by a team of international experts Machine Learning and Data Mining presents an exciting contribution addressing the new challenge. It provides an introduction to basic methods and its coverage spans the analysis of numerical data, text, images and sound. Most impressively, the book describes applications across a wide spectrum of real world problems, in domains such as engineering, control, biology, medicine and music
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 andMultistrategy Approach (R. Michalski & K. Kaufman).
Fielded Applications of Machine Learning (P. Langley & H.Simon).
Applications of Inductive Logic Programming (I. Bratko, etal.).
DESIGN AND ENGINEERING.
Application of Machine Learning in Finite Element Computation (B.Dolsak, et al.).
Application of Inductive Learning and Case-Based Reasoning forTroubleshooting Industrial Machines (M. Manago & E.Auriol).
Empirical Assembly Sequence Planning: A Multistrategy Constructivelearning Approach (H. Ko).
Inductive Learning in Design: A Method and Case Study ConcerningDesign 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: EmpiricalInvestigations 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 RiverWater Quality (S. Deroski, et al.).
Index.
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