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JMLR
2006
89views more  JMLR 2006»
13 years 9 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
ACL
2010
13 years 7 months ago
Distributional Similarity vs. PU Learning for Entity Set Expansion
Distributional similarity is a classic technique for entity set expansion, where the system is given a set of seed entities of a particular class, and is asked to expand the set u...
Xiaoli Li, Lei Zhang, Bing Liu, See-Kiong Ng
DICTA
2003
13 years 10 months ago
Algebraic Curve Fitting Support Vector Machines
An algebraic curve is defined as the zero set of a multivariate polynomial. We consider the problem of fitting an algebraic curve to a set of vectors given an additional set of v...
Christian J. Walder, Brian C. Lovell, Peter J. Koo...
RSFDGRC
2005
Springer
151views Data Mining» more  RSFDGRC 2005»
14 years 2 months ago
DIXER - Distributed Executor for Rough Set Exploration System
We present the Distributed Executor for RSES (DIXER) which is a supplementary software for the Rough Set Exploration System (RSES). It takes an advantage of grid computing paradigm...
Jan G. Bazan, Rafal Latkowski, Marcin S. Szczuka
ICNC
2009
Springer
14 years 1 months ago
Knowledge Acquisition Approach Based on Rough Set and Artificial Neural Network in Product Design Process
In this paper, product structure is taken as knowledge acquisition point, and the effective knowledge acquisition path is discussed by establishing the associated relationship bet...
Changfeng Yuan, Wanlei Wang, Yan Chen