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» A Bayesian Metric for Evaluating Machine Learning Algorithms
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ICIP
2008
IEEE
16 years 2 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
131
Voted
ICMLA
2010
14 years 10 months ago
Smoothing Gene Expression Using Biological Networks
Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify inform...
Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
90
Voted
ICML
2004
IEEE
16 years 1 months ago
SVM-based generalized multiple-instance learning via approximate box counting
The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based imageretrieval. Recently, a generalization of thi...
Qingping Tao, Stephen D. Scott, N. V. Vinodchandra...
104
Voted
ICML
2007
IEEE
16 years 1 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
ECML
2006
Springer
15 years 4 months ago
Automatically Evolving Rule Induction Algorithms
Research in the rule induction algorithm field produced many algorithms in the last 30 years. However, these algorithms are usually obtained from a few basic rule induction algorit...
Gisele L. Pappa, Alex Alves Freitas