
Sparse Learning Under Regularization Framework
Theory and Applications
LAP Lambert Academic Publishing
Published on 15. April 2011
Book
Paperback/Softback
152 pages
978-3-8443-3030-4 (ISBN)
Description
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.
More details
Language
English
Place of publication
Germany
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 220 mm
Width: 150 mm
Thickness: 10 mm
Weight
244 gr
ISBN-13
978-3-8443-3030-4 (9783844330304)
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Schweitzer Classification
Persons
Haiqin Yang finished his Ph.D. study in Computer Science and Engineering, the Chinese University of Hong Kong in 2010. His research interests include machine learning, data mining, financial engineering, pattern recognition, etc. He has conducted various research work in these areas and output many publications and patents.