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ICPR
2000
IEEE
13 years 12 months ago
Scaling-Up Support Vector Machines Using Boosting Algorithm
In the recent years support vector machines (SVMs) have been successfully applied to solve a large number of classification problems. Training an SVM, usually posed as a quadrati...
Dmitry Pavlov, Jianchang Mao, Byron Dom
AAAI
2006
13 years 8 months ago
Efficient L1 Regularized Logistic Regression
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized...
Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. N...
JMLR
2006
89views more  JMLR 2006»
13 years 7 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
IOR
2011
175views more  IOR 2011»
13 years 2 months ago
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem
This paper introduces and studies the maximum k-plex problem, which arises in social network analysis and has wider applicability in several important areas employing graph-based ...
Balabhaskar Balasundaram, Sergiy Butenko, Illya V....
ICCV
2009
IEEE
15 years 13 days ago
Large Displacement Optical Flow Computation without Warping
We propose an algorithm for large displacement opti- cal flow estimation which does not require the commonly used coarse-to-fine warping strategy. It is based on a quadratic rel...
Frank Steinbrucker, Thomas Pock, Daniel Cremers