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» Active Learning for Class Probability Estimation and Ranking
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ICML
2004
IEEE
16 years 14 days ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
DAGM
2009
Springer
15 years 6 months ago
Learning with Few Examples by Transferring Feature Relevance
The human ability to learn difficult object categories from just a few views is often explained by an extensive use of knowledge from related classes. In this work we study the use...
Erik Rodner, Joachim Denzler
BIOCOMP
2008
15 years 1 months ago
Analysis of Protein-Ligand Interactions Using Localized Stereochemical Features
Computational analyses of protein structure-function relationships have traditionally been based on sequence homology, fold family analysis and 3D motifs/templates. Previous struct...
Reetal Pai, James C. Sacchettini, Thomas R. Ioerge...
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KDD
2009
ACM
191views Data Mining» more  KDD 2009»
16 years 6 days ago
Scalable pseudo-likelihood estimation in hybrid random fields
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
Antonino Freno, Edmondo Trentin, Marco Gori
GECCO
2006
Springer
192views Optimization» more  GECCO 2006»
15 years 3 months ago
Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms
This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Lear...
Andrei Petrovski, Siddhartha Shakya, John A. W. Mc...