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172
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ICML
2009
IEEE
16 years 7 months ago
2009
IEEE
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
172
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ICML
2009
IEEE
16 years 7 months ago
2009
IEEE
There are well known algorithms for learning the structure of directed and undirected graphical models from data, but nearly all assume that the data consists of a single i.i.d. s...
184
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ICML
2007
IEEE
16 years 7 months ago
2007
IEEE
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
151
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ICML
2008
IEEE
16 years 7 months ago
2008
IEEE
In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the mech...
166
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KDD
2005
ACM
16 years 7 months ago
2005
ACM
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...