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207
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TNN
2010
143views Management» more  TNN 2010»
15 years 1 months ago
Using unsupervised analysis to constrain generalization bounds for support vector classifiers
Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
Sergio Decherchi, Sandro Ridella, Rodolfo Zunino, ...
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
16 years 7 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 7 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
VLDB
2007
ACM
129views Database» more  VLDB 2007»
16 years 21 days ago
Processing Forecasting Queries
Forecasting future events based on historic data is useful in many domains like system management, adaptive query processing, environmental monitoring, and financial planning. We...
Songyun Duan, Shivnath Babu
171
Voted
COLT
2001
Springer
15 years 11 months ago
Geometric Bounds for Generalization in Boosting
We consider geometric conditions on a labeled data set which guarantee that boosting algorithms work well when linear classifiers are used as weak learners. We start by providing ...
Shie Mannor, Ron Meir