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

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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.

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Product Details

  • ISBN-13: 9780471971993
  • Publisher: Wiley
  • Publication date: 8/14/1998
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 472
  • Product dimensions: 1.13 (w) x 9.21 (h) x 6.14 (d)

Table of Contents


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.).


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).


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).


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).


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.).


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