Machine Learning and Data Mining
Methods and Applications
Published on 29. January 1998
Book
Hardback
XVI, 456 pages
978-0-471-97199-3 (ISBN)
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Article is exhausted; no reprint
Description
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.
Die Suche nach Rechenmethoden, die die Problemlösungsstrategien des menschlichen Gehirns imitieren - das ist Machine Learning, ein Thema, das bisher in der Literatur eher stiefmütterlich behandelt wurde. Dieses Buch wendet sich besonders an Nicht-Computerfachleute, die Alternativen zu traditionellen Methoden der Problemlösung suchen. (03/98)
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.
Die Suche nach Rechenmethoden, die die Problemlösungsstrategien des menschlichen Gehirns imitieren - das ist Machine Learning, ein Thema, das bisher in der Literatur eher stiefmütterlich behandelt wurde. Dieses Buch wendet sich besonders an Nicht-Computerfachleute, die Alternativen zu traditionellen Methoden der Problemlösung suchen. (03/98)
More details
Product info
gebunden
Edition
1. Auflage
Language
English
Place of publication
Chichester
United Kingdom
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 25.8 cm
Width: 18.6 cm
Thickness: 3.2 cm
Weight
976 gr
ISBN-13
978-0-471-97199-3 (9780471971993)
Schweitzer Classification
Persons
Ryszard S. Michalski was a Polish-American computer scientist. Michalski was Professor at George Mason University and a pioneer in the field of machine learning. Ivan Bratko is the editor of Machine Learning and Data Mining: Methods and Applications, published by Wiley.
Content
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.
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.