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» On learning algorithm selection for classification
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STOC
2003
ACM
154views Algorithms» more  STOC 2003»
16 years 6 months ago
Boosting in the presence of noise
Boosting algorithms are procedures that "boost" low-accuracy weak learning algorithms to achieve arbitrarily high accuracy. Over the past decade boosting has been widely...
Adam Kalai, Rocco A. Servedio
MCS
2009
Springer
16 years 1 months ago
Selective Ensemble under Regularization Framework
An ensemble is generated by training multiple component learners for a same task and then combining them for predictions. It is known that when lots of trained learners are availab...
Nan Li, Zhi-Hua Zhou
NECO
2007
90views more  NECO 2007»
15 years 6 months ago
Neighborhood Property-Based Pattern Selection for Support Vector Machines
Support Vector Machine (SVM) has been spotlighted in the machine learning community thanks to its theoretical soundness and practical performance. When applied to a large data set...
Hyunjung Shin, Sungzoon Cho
ICDM
2005
IEEE
153views Data Mining» more  ICDM 2005»
16 years 6 days ago
Speculative Markov Blanket Discovery for Optimal Feature Selection
In this paper we address the problem of learning the Markov blanket of a quantity from data in an efficient manner. Markov blanket discovery can be used in the feature selection ...
Sandeep Yaramakala, Dimitris Margaritis
ICML
2010
IEEE
15 years 7 months ago
Large Scale Max-Margin Multi-Label Classification with Priors
We propose a max-margin formulation for the multi-label classification problem where the goal is to tag a data point with a set of pre-specified labels. Given a set of L labels, a...
Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vis...