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» Choosing Multiple Parameters for Support Vector Machines
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JMLR
2008
140views more  JMLR 2008»
13 years 7 months ago
Aggregation of SVM Classifiers Using Sobolev Spaces
This paper investigates statistical performances of Support Vector Machines (SVM) and considers the problem of adaptation to the margin parameter and to complexity. In particular ...
Sébastien Loustau
KDD
2012
ACM
205views Data Mining» more  KDD 2012»
11 years 10 months ago
Rank-loss support instance machines for MIML instance annotation
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Forrest Briggs, Xiaoli Z. Fern, Raviv Raich
ICML
2005
IEEE
14 years 8 months ago
Dynamic preferences in multi-criteria reinforcement learning
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
Sriraam Natarajan, Prasad Tadepalli
BMCBI
2007
142views more  BMCBI 2007»
13 years 7 months ago
Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary informa
Background: In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human prote...
Mamoon Rashid, Sudipto Saha, Gajendra P. S. Raghav...
BMCBI
2006
110views more  BMCBI 2006»
13 years 7 months ago
Bias in error estimation when using cross-validation for model selection
Background: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers...
Sudhir Varma, Richard Simon