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JMLR
2008
150views more  JMLR 2008»
15 years 2 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
16 years 3 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
16 years 4 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»
16 years 2 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
15 years 8 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