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» Approximation algorithms for budgeted learning problems
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SDM
2009
SIAM
119views Data Mining» more  SDM 2009»
14 years 5 months ago
Twin Vector Machines for Online Learning on a Budget.
This paper proposes Twin Vector Machine (TVM), a constant space and sublinear time Support Vector Machine (SVM) algorithm for online learning. TVM achieves its favorable scaling b...
Zhuang Wang, Slobodan Vucetic
FSTTCS
1993
Springer
14 years 16 days ago
Compact Location Problems
We investigate the complexity and approximability of some location problems when two distance values are specified for each pair of potential sites. These problems involve the se...
Venkatesh Radhakrishnan, Sven Oliver Krumke, Madha...
CVPR
2010
IEEE
14 years 4 months ago
Far-Sighted Active Learning on a Budget for Image and Video Recognition
Active learning methods aim to select the most informative unlabeled instances to label first, and can help to focus image or video annotations on the examples that will most impr...
Sudheendra Vijayanarasimhan, Prateek Jain, Kristen...
WINE
2007
Springer
124views Economy» more  WINE 2007»
14 years 2 months ago
Stochastic Models for Budget Optimization in Search-Based Advertising
Internet search companies sell advertisement slots based on users’ search queries via an auction. Advertisers have to solve a complex optimization problem of how to place bids o...
S. Muthukrishnan, Martin Pál, Zoya Svitkina
SIAMCOMP
2008
140views more  SIAMCOMP 2008»
13 years 8 months ago
The Forgetron: A Kernel-Based Perceptron on a Budget
Abstract. The Perceptron algorithm, despite its simplicity, often performs well in online classification tasks. The Perceptron becomes especially effective when it is used in conju...
Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer