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151
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ML
2002
ACM
178views Machine Learning» more  ML 2002»
15 years 8 days ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
102
Voted
ER
2003
Springer
148views Database» more  ER 2003»
15 years 5 months ago
Improving Query Performance Using Materialized XML Views: A Learning-Based Approach
We consider the problem of improving the efficiency of query processing on an XML interface of a relational database, for predefined query workloads. The main contribution of this ...
Ashish Shah, Rada Chirkova
120
Voted
EMNLP
2010
14 years 10 months ago
Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models
We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to partof-speech (POS) tagging. The algorithm uses a simil...
Amarnag Subramanya, Slav Petrov, Fernando Pereira
CVPR
2005
IEEE
16 years 2 months ago
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support ef...
Dragomir Anguelov, Benjamin Taskar, Vassil Chatalb...
WEBDB
2010
Springer
155views Database» more  WEBDB 2010»
15 years 5 months ago
Learning Topical Transition Probabilities in Click Through Data with Regression Models
The transition of search engine users’ intents has been studied for a long time. The knowledge of intent transition, once discovered, can yield a better understanding of how diļ...
Xiao Zhang, Prasenjit Mitra