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» Learning with Rigorous Support Vector Machines
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CASCON
2001
148views Education» more  CASCON 2001»
15 years 5 months ago
Email classification with co-training
The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algo...
Svetlana Kiritchenko, Stan Matwin
NIPS
2001
15 years 5 months ago
Covariance Kernels from Bayesian Generative Models
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
Matthias Seeger
ESA
2010
Springer
227views Algorithms» more  ESA 2010»
15 years 5 months ago
Approximating Parameterized Convex Optimization Problems
We consider parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an -approximat...
Joachim Giesen, Martin Jaggi, Sören Laue
131
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MM
2005
ACM
172views Multimedia» more  MM 2005»
15 years 9 months ago
Learning the semantics of multimedia queries and concepts from a small number of examples
In this paper we unify two supposedly distinct tasks in multimedia retrieval. One task involves answering queries with a few examples. The other involves learning models for seman...
Apostol Natsev, Milind R. Naphade, Jelena Tesic
EMNLP
2004
15 years 5 months ago
Max-Margin Parsing
We present a novel discriminative approach to parsing inspired by the large-margin criterion underlying support vector machines. Our formulation uses a factorization analogous to ...
Ben Taskar, Dan Klein, Mike Collins, Daphne Koller...