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» A Boosting Algorithm for Regression
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137
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BIBE
2007
IEEE
124views Bioinformatics» more  BIBE 2007»
15 years 10 months ago
Finding Cancer-Related Gene Combinations Using a Molecular Evolutionary Algorithm
—High-throughput data such as microarrays make it possible to investigate the molecular-level mechanism of cancer more efficiently. Computational methods boost the microarray ana...
Chan-Hoon Park, Soo-Jin Kim, Sun Kim, Dong-Yeon Ch...
161
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NMR
2004
Springer
15 years 9 months ago
A SAT-based polynomial space algorithm for answer set programming
The relation between answer set programming (ASP) and propositional satisfiability (SAT) is at the center of many research papers, partly because of the tremendous performance bo...
Enrico Giunchiglia, Yuliya Lierler, Marco Maratea
131
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ML
2000
ACM
185views Machine Learning» more  ML 2000»
15 years 3 months ago
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms
Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the ca...
Tjen-Sien Lim, Wei-Yin Loh, Yu-Shan Shih
113
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ICML
2003
IEEE
16 years 4 months ago
Online Ranking/Collaborative Filtering Using the Perceptron Algorithm
In this paper we present a simple to implement truly online large margin version of the Perceptron ranking (PRank) algorithm, called the OAP-BPM (Online Aggregate Prank-Bayes Poin...
Edward F. Harrington
149
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CSDA
2007
136views more  CSDA 2007»
15 years 3 months ago
A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation
The maximum rank correlation (MRC) estimator was originally studied by Han [1987. Nonparametric analysis of a generalized regression model. J. Econometrics 35, 303–316] and Sher...
Hansheng Wang