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» Generalization Error Bounds Using Unlabeled Data
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NIPS
2007
14 years 11 months ago
A general agnostic active learning algorithm
We present a simple, agnostic active learning algorithm that works for any hypothesis class of bounded VC dimension, and any data distribution. Our algorithm extends a scheme of C...
Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni
SDM
2007
SIAM
81views Data Mining» more  SDM 2007»
14 years 11 months ago
A PAC Bound for Approximate Support Vector Machines
We study a class of algorithms that speed up the training process of support vector machines (SVMs) by returning an approximate SVM. We focus on algorithms that reduce the size of...
Dongwei Cao, Daniel Boley
SDM
2010
SIAM
166views Data Mining» more  SDM 2010»
14 years 11 months ago
A Permutation Approach to Validation
We give a permutation approach to validation (estimation of out-sample error). One typical use of validation is model selection. We establish the legitimacy of the proposed permut...
Malik Magdon-Ismail, Konstantin Mertsalov
CVPR
2008
IEEE
15 years 11 months ago
A joint appearance-spatial distance for kernel-based image categorization
The goal of image categorization is to classify a collection of unlabeled images into a set of predefined classes to support semantic-level image retrieval. The distance measures ...
Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Jinhui Tang,...
NETWORKS
2008
14 years 9 months ago
A Reach and Bound algorithm for acyclic dynamic-programming networks
Node pruning is a commonly used technique for solution acceleration in a dynamic programming network. In pruning, nodes are adaptively removed from the dynamic programming network...
Matthew D. Bailey, Robert L. Smith, Jeffrey M. Ald...