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JMLR
2008
150views more  JMLR 2008»
13 years 8 months ago
Discriminative Learning of Max-Sum Classifiers
The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and obser...
Vojtech Franc, Bogdan Savchynskyy
DAC
2006
ACM
14 years 9 months ago
Optimal cell flipping in placement and floorplanning
In a placed circuit, there are a lot of movable cells that can be flipped to further reduce the total wirelength, without affecting the original placement solution. We aim at solv...
Chiu-Wing Sham, Evangeline F. Y. Young, Chris C. N...
CVPR
1998
IEEE
14 years 11 months ago
Markov Random Fields with Efficient Approximations
Markov Random Fields (MRF's) can be used for a wide variety of vision problems. In this paper we focus on MRF's with two-valued clique potentials, which form a generaliz...
Yuri Boykov, Olga Veksler, Ramin Zabih
KDD
2005
ACM
117views Data Mining» more  KDD 2005»
14 years 9 months ago
Rule extraction from linear support vector machines
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
ICALP
2005
Springer
14 years 2 months ago
Linear Time Algorithms for Clustering Problems in Any Dimensions
Abstract. We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certa...
Amit Kumar, Yogish Sabharwal, Sandeep Sen