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154
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ALT
2006
Springer
16 years 17 days ago
Learning Linearly Separable Languages
This paper presents a novel paradigm for learning languages that consists of mapping strings to an appropriate high-dimensional feature space and learning a separating hyperplane i...
Leonid Kontorovich, Corinna Cortes, Mehryar Mohri
163
Voted
KDD
2003
ACM
180views Data Mining» more  KDD 2003»
16 years 4 months ago
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient ...
Hwanjo Yu, Jiong Yang, Jiawei Han
131
Voted
ICASSP
2010
IEEE
15 years 3 months ago
Characterization of movie genre based on music score
While it is clear that the full emotional effect of a movie scene is carried through the successful interpretation of audio and visual information, music still carries a significa...
Aida Austin, Elliot Moore II, Udit Gupta, Parag Ch...
ICML
2004
IEEE
16 years 4 months ago
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5
Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge numbers of features. Most previous studies found that the major...
Evgeniy Gabrilovich, Shaul Markovitch
133
Voted
ICML
2000
IEEE
16 years 4 months ago
Duality and Geometry in SVM Classifiers
We develop an intuitive geometric interpretation of the standard support vector machine (SVM) for classification of both linearly separable and inseparable data and provide a rigo...
Kristin P. Bennett, Erin J. Bredensteiner