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» Finding Structure in Reinforcement Learning
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112
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JMLR
2008
159views more  JMLR 2008»
14 years 11 months ago
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
Existing template-independent web data extraction approaches adopt highly ineffective decoupled strategies--attempting to do data record detection and attribute labeling in two se...
Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen
CORR
2010
Springer
163views Education» more  CORR 2010»
14 years 10 months ago
Faster Rates for training Max-Margin Markov Networks
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3 N) is an effective approach. All state-of-...
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
128
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ACL
2010
14 years 9 months ago
Starting from Scratch in Semantic Role Labeling
A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituent...
Michael Connor, Yael Gertner, Cynthia Fisher, Dan ...
SWWS
2008
15 years 1 months ago
A Harmony based Adaptive Ontology Mapping Approach
- Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. Ontology mapping is critical to achieve semantic interoperability in the...
Ming Mao, Yefei Peng, Michael Spring
NIPS
1998
15 years 1 months ago
SMEM Algorithm for Mixture Models
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Ge...