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Feature selection for support vector machines by alignment with ideal kernel

Author
Català Roig, N.; Martin, M.
Type of activity
Report
Date
2007-12
Code
LSI-07-48-R
Repository
http://hdl.handle.net/2117/86457 Open in new window
Abstract
Feature selection has several potentially beneficial uses in machine learning. Some of them are to improve the performance of the learning method by removing noisy features, to reduce the feature set in data collection, and to better understand the data. In this report we present how to use empirical alignment, a well known measure for the fitness of kernels to data labels, to perform feature selection for support vector machines. We show that this measure improves the results obtained with othe...
Citation
Català, N., Martín, M. "Feature selection for support vector machines by alignment with ideal kernel". 2007.
Keywords
Empirical alignment, Feature selection, Kernel method
Group of research
GPLN - Natural Language Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group
TALP - Centre for Language and Speech Technologies and Applications

Participants

Attachments