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ICCV
2007
IEEE
14 years 9 months ago
Boosting Invariance and Efficiency in Supervised Learning
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
Andrea Vedaldi, Paolo Favaro, Enrico Grisan
EACL
2009
ACL Anthology
14 years 8 months ago
Sentiment Summarization: Evaluating and Learning User Preferences
We present the results of a large-scale, end-to-end human evaluation of various sentiment summarization models. The evaluation shows that users have a strong preference for summar...
Kevin Lerman, Sasha Blair-Goldensohn, Ryan T. McDo...
ICML
2001
IEEE
14 years 8 months ago
Learning with the Set Covering Machine
We generalize the classical algorithms of Valiant and Haussler for learning conjunctions and disjunctions of Boolean attributes to the problem of learning these functions over arb...
Mario Marchand, John Shawe-Taylor
AIRS
2010
Springer
13 years 5 months ago
Relevance Ranking Using Kernels
This paper is concerned with relevance ranking in search, particularly that using term dependency information. It proposes a novel and unified approach to relevance ranking using ...
Jun Xu, Hang Li, Chaoliang Zhong
ICML
2005
IEEE
14 years 8 months ago
New kernels for protein structural motif discovery and function classification
We present new, general-purpose kernels for protein structure analysis, and describe how to apply them to structural motif discovery and function classification. Experiments show ...
Chang Wang, Stephen D. Scott