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

Erscheinungsjahr 2005
ISBN: 978-3-8325-0809-8
Verlag: Logos

Buch, Englisch, 150 Seiten, Format (B × H): 170 mm x 240 mm

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.

Autoren/Hrsg.

Iswanto, Bambang Heru

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein E-Learning
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik E-Learning, Bildungstechnologie