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» Learning Polyhedral Classifiers Using Logistic Function
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AUSAI
2003
Springer
15 years 3 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»
14 years 11 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
14 years 10 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
15 years 1 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»
14 years 11 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