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» A theory of learning with similarity functions
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COLT
2000
Springer
15 years 4 months ago
PAC Analogues of Perceptron and Winnow via Boosting the Margin
We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...
Rocco A. Servedio
CORR
2004
Springer
144views Education» more  CORR 2004»
14 years 11 months ago
The Google Similarity Distance
Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is...
Rudi Cilibrasi, Paul M. B. Vitányi
BMCBI
2008
228views more  BMCBI 2008»
14 years 12 months ago
Adaptive diffusion kernel learning from biological networks for protein function prediction
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Liang Sun, Shuiwang Ji, Jieping Ye
MICCAI
2009
Springer
16 years 27 days ago
Task-Optimal Registration Cost Functions
Abstract. In this paper, we propose a framework for learning the parameters of registration cost functions ? such as the tradeoff between the regularization and image similiarity t...
B. T. Thomas Yeo, Mert R. Sabuncu, Polina Gollan...
KDD
2003
ACM
214views Data Mining» more  KDD 2003»
16 years 4 days ago
Adaptive duplicate detection using learnable string similarity measures
The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied...
Mikhail Bilenko, Raymond J. Mooney