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» Learning from General Label Constraints
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NIPS
2003
13 years 10 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller
ICML
2004
IEEE
14 years 9 months ago
A multiplicative up-propagation algorithm
We present a generalization of the nonnegative matrix factorization (NMF), where a multilayer generative network with nonnegative weights is used to approximate the observed nonne...
Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh
MLG
2007
Springer
14 years 2 months ago
A Universal Kernel for Learning Regular Languages
We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but...
Leonid Kontorovich
KDD
2005
ACM
139views Data Mining» more  KDD 2005»
14 years 2 months ago
Learning to predict train wheel failures
This paper describes a successful but challenging application of data mining in the railway industry. The objective is to optimize maintenance and operation of trains through prog...
Chunsheng Yang, Sylvain Létourneau
ICML
2004
IEEE
14 years 9 months ago
Boosting margin based distance functions for clustering
The performance of graph based clustering methods critically depends on the quality of the distance function, used to compute similarities between pairs of neighboring nodes. In t...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall