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» The Inefficiency of Batch Training for Large Training Sets
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ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
13 years 9 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...
PAMI
2006
206views more  PAMI 2006»
13 years 8 months ago
MILES: Multiple-Instance Learning via Embedded Instance Selection
Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called...
Yixin Chen, Jinbo Bi, James Ze Wang
TIT
1998
80views more  TIT 1998»
13 years 8 months ago
Structural Risk Minimization Over Data-Dependent Hierarchies
The paper introduces some generalizations of Vapnik’s method of structural risk minimisation (SRM). As well as making explicit some of the details on SRM, it provides a result t...
John Shawe-Taylor, Peter L. Bartlett, Robert C. Wi...
ICDAR
2009
IEEE
13 years 6 months ago
Stochastic Segment Modeling for Offline Handwriting Recognition
In this paper, we present a novel approach for incorporating structural information into the hidden Markov Modeling (HMM) framework for offline handwriting recognition. Traditiona...
Premkumar Natarajan, Krishna Subramanian, Anurag B...
IJBRA
2010
133views more  IJBRA 2010»
13 years 6 months ago
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features and ...
Mona Soliman Habib, Jugal Kalita