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» Using Problems to Learn Service-Oriented Computing
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AI
2000
Springer
15 years 6 months ago
Learning to construct knowledge bases from the World Wide Web
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a...
Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew M...
CVPR
2009
IEEE
17 years 1 months ago
Contextual Classification with Functional Max-Margin Markov Networks
We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To...
Daniel Munoz, James A. Bagnell, Martial Hebert, Ni...
IJCNN
2007
IEEE
16 years 13 days ago
Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent
—The 0/1 loss is an important cost function for perceptrons. Nevertheless it cannot be easily minimized by most existing perceptron learning algorithms. In this paper, we propose...
Ling Li, Hsuan-Tien Lin
144
Voted
COLT
2007
Springer
16 years 10 days ago
Sketching Information Divergences
When comparing discrete probability distributions, natural measures of similarity are not p distances but rather are informationdivergences such as Kullback-Leibler and Hellinger. ...
Sudipto Guha, Piotr Indyk, Andrew McGregor
178
Voted
ICML
1989
IEEE
15 years 10 months ago
Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization
Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out, but first-order Horn logic i...
Scott Dietzen, Frank Pfenning