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» Scaling-Up Support Vector Machines Using Boosting Algorithm
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JMLR
2006
89views more  JMLR 2006»
15 years 2 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
BMCBI
2005
120views more  BMCBI 2005»
15 years 2 months ago
pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties
Background: Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of pre...
Deepak Sarda, Gek Huey Chua, Kuo-Bin Li, Arun Kris...
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KDD
2005
ACM
118views Data Mining» more  KDD 2005»
16 years 2 months ago
On the use of linear programming for unsupervised text classification
We propose a new algorithm for dimensionality reduction and unsupervised text classification. We use mixture models as underlying process of generating corpus and utilize a novel,...
Mark Sandler
IFIP12
2010
15 years 28 days ago
Learning the Preferences of News Readers with SVM and Lasso Ranking
We attack the task of predicting which news-stories are more appealing to a given audience by comparing ‘most popular stories’, gathered from various online news outlets, over ...
Elena Hensinger, Ilias N. Flaounas, Nello Cristian...
EMNLP
2004
15 years 3 months ago
Max-Margin Parsing
We present a novel discriminative approach to parsing inspired by the large-margin criterion underlying support vector machines. Our formulation uses a factorization analogous to ...
Ben Taskar, Dan Klein, Mike Collins, Daphne Koller...