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186
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KDD
2004
ACM
151views Data Mining» more  KDD 2004»
16 years 7 months ago
Feature selection in scientific applications
Numerous applications of data mining to scientific data involve the induction of a classification model. In many cases, the collection of data is not performed with this task in m...
Erick Cantú-Paz, Shawn Newsam, Chandrika Ka...
172
Voted
ICML
2009
IEEE
16 years 7 months ago
Graph construction and b-matching for semi-supervised learning
Graph based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems. A crucial step in graph based SSL methods is the conv...
Tony Jebara, Jun Wang, Shih-Fu Chang
ICDM
2005
IEEE
138views Data Mining» more  ICDM 2005»
16 years 14 days ago
On Feature Selection through Clustering
We study an algorithm for feature selection that clusters attributes using a special metric and then makes use of the dendrogram of the resulting cluster hierarchy to choose the m...
Richard Butterworth, Gregory Piatetsky-Shapiro, Da...
165
Voted
AUSDM
2006
Springer
125views Data Mining» more  AUSDM 2006»
15 years 10 months ago
Investigating the Size and Value Effect in Determining Performance of Australian Listed Companies: A Neural Network Approach
This paper explores the size and value effect in influencing performance of individual companies using backpropagation neural networks. According to existing theory, companies wit...
Justin Luu, Paul J. Kennedy
PAKDD
2000
ACM
100views Data Mining» more  PAKDD 2000»
15 years 10 months ago
Discovery of Relevant Weights by Minimizing Cross-Validation Error
In order to discover relevant weights of neural networks, this paper proposes a novel method to learn a distinct squared penalty factor for each weight as a minimization problem ov...
Kazumi Saito, Ryohei Nakano