Sparse Learning Under Regularization Framework

Language: English

Published by LAP LAMBERT Academic Publishing Apr 2011, 2011

3844330305 / 9783844330304

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This item is printed on demand - Print on Demand Titel. Neuware -Regularization is a dominant theme in machine learning and statistics due to its prominent ability in providing an intuitive and principled tool for learning from high-dimensional data. As large-scale learning applications become popular, developing efficient algorithms and parsimonious models become promising and necessary for these applications. Aiming at solving large-scale learning problems, this book tackles the key research problems ranging from feature selection to learning with mixed unlabeled data and learning data similarity representation. More specifically, we focus on the problems in three areas: online learning, semi-supervised learning, and multiple kernel learning. The proposed models can be applied in various applications, including marketing analysis, bioinformatics, pattern recognition, etc.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.

Seller Inventory # 9783844330304

Title
Sparse Learning Under Regularization Framework
Author
Haiqin Yang
Publisher
LAP LAMBERT Academic Publishing Apr 2011
Publication year
2011
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3844330305
ISBN 13
9783844330304
Item weight
244 grams
Dimensions
220x150x10 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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