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» Redundancy based feature selection for microarray data
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NIPS
2007
14 years 11 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
ICPR
2006
IEEE
15 years 11 months ago
Scalable Representative Instance Selection and Ranking
Finding a small set of representative instances for large datasets can bring various benefits to data mining practitioners so they can (1) build a learner superior to the one cons...
Xindong Wu, Xingquan Zhu
BMCBI
2005
201views more  BMCBI 2005»
14 years 9 months ago
Principal component analysis for predicting transcription-factor binding motifs from array-derived data
Background: The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to...
Yunlong Liu, Matthew P. Vincenti, Hiroki Yokota
BMCBI
2007
111views more  BMCBI 2007»
14 years 10 months ago
MotifCombinator: a web-based tool to search for combinations of cis-regulatory motifs
Background: A combination of multiple types of transcription factors and cis-regulatory elements is often required for gene expression in eukaryotes, and the combinatorial regulat...
Mamoru Kato, Tatsuhiko Tsunoda
ISNN
2011
Springer
14 years 23 days ago
Orthogonal Feature Learning for Time Series Clustering
This paper presents a new method that uses orthogonalized features for time series clustering and classification. To cluster or classify time series data, either original data or...
Xiaozhe Wang, Leo Lopes