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BMCBI
2005
190views more  BMCBI 2005»
15 years 1 months ago
An Entropy-based gene selection method for cancer classification using microarray data
Background: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of n...
Xiaoxing Liu, Arun Krishnan, Adrian Mondry
ML
1998
ACM
15 years 1 months ago
On Restricted-Focus-of-Attention Learnability of Boolean Functions
In the k-Restricted-Focus-of-Attention (k-RFA) model, only k of the n attributes of each example are revealed to the learner, although the set of visible attributes in each example...
Andreas Birkendorf, Eli Dichterman, Jeffrey C. Jac...
GECCO
2010
Springer
168views Optimization» more  GECCO 2010»
15 years 6 months ago
Investigating whether hyperNEAT produces modular neural networks
HyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a ionally efficient high-level abstraction of development....
Jeff Clune, Benjamin E. Beckmann, Philip K. McKinl...
BMCBI
2006
142views more  BMCBI 2006»
15 years 1 months ago
Improving the Performance of SVM-RFE to Select Genes in Microarray Data
Background: Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. ...
Yuanyuan Ding, Dawn Wilkins
BMCBI
2010
176views more  BMCBI 2010»
15 years 1 months ago
Reverse engineering gene regulatory network from microarray data using linear time-variant model
nd: Gene regulatory network is an abstract mapping of gene regulations in living cells that can help to predict the system behavior of living organisms. Such prediction capability...
Mitra Kabir, Nasimul Noman, Hitoshi Iba