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New Algorithms for Learning of Mixture Models and Their Application for Classification and Density Estimation

Bambang Heru Iswanto

ISBN 978-3-8325-0809-8
150 pages, year of publication: 2005
price: 40.50 €
Mixture model is known as a convenient way for modelling the probability density function in statistics. Recently, the method is adopted by machine learning communities in a variety of application settings such as cluster analysis, classification, density estimation and function approximation. This book concerns with learning algorithms of the mixture models for density estimation and classification tasks. Special attention is given for the semi-supervised learning and active learning methods which are very important in many practical settings. The presented learning methods attempt to reduce the size of labelled data sets required to achieve certain level of classification performance.

Keywords:
  • Active Learning
  • Mixture Models
  • Semi-Superviseo Learning
  • EM Algorithm

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40.50 €