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» Learning Polyhedral Classifiers Using Logistic Function
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AUSAI
2003
Springer
13 years 11 months ago
On Why Discretization Works for Naive-Bayes Classifiers
We investigate why discretization is effective in naive-Bayes learning. We prove a theorem that identifies particular conditions under which discretization will result in naiveBay...
Ying Yang, Geoffrey I. Webb
CLASSIFICATION
2004
59views more  CLASSIFICATION 2004»
13 years 7 months ago
Oscillation Heuristics for the Two-group Classification Problem
: We propose a new nonparametric family of oscillation heuristics for improving linear classifiers in the two-group discriminant problem. The heuristics are motivated by the intuit...
Ognian Asparouhov, Paul A. Rubin
WIOPT
2010
IEEE
13 years 5 months ago
Enhancing RRM optimization using a priori knowledge for automated troubleshooting
—The paper presents a methodology that combines statistical learning with constraint optimization by locally optimizing Radio Resource Management (RRM) or system parameters of po...
Moazzam Islam Tiwana, Zwi Altman, Berna Sayra&cced...
ICMLA
2008
13 years 9 months ago
Text Classification Using Tree Kernels and Linguistic Information
Standard Machine Learning approaches to text classification use the bag-of-words representation of documents to deceive the classification target function. Typical linguistic stru...
Teresa Gonçalves, Paulo Quaresma
BMCBI
2006
111views more  BMCBI 2006»
13 years 7 months ago
PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions
Background: Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples...
Tomer Hertz, Chen Yanover