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» Experimental Design for Variable Selection in Data Bases
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NIPS
2007
15 years 7 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
CLUSTER
2007
IEEE
16 years 11 days ago
The design of MPI based distributed shared memory systems to support OpenMP on clusters
— OpenMP can be supported in cluster environments by using distributed shared memory (DSM) systems. A portable approach for building DSM systems is to layer it on MPI. With these...
H'sien J. Wong, Alistair P. Rendell
GECCO
2009
Springer
148views Optimization» more  GECCO 2009»
16 years 18 days ago
An evolutionary approach to constructive induction for link discovery
This paper presents a genetic programming-based symbolic regression approach to the construction of relational features in link analysis applications. Specifically, we consider t...
Tim Weninger, William H. Hsu, Jing Xia, Waleed Alj...
MIR
2010
ACM
167views Multimedia» more  MIR 2010»
16 years 26 days ago
Improving automatic music classification performance by extracting features from different types of data
This paper discusses two sets of automatic musical genre classification experiments. Promising research directions are then proposed based on the results of these experiments. The...
Cory McKay, Ichiro Fujinaga
SDM
2008
SIAM
157views Data Mining» more  SDM 2008»
15 years 7 months ago
ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data
Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Suc...
M. Maruf Hossain, Md. Rafiul Hassan, James Bailey