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» Learning aspect models with partially labeled data
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CVPR
2009
IEEE
15 years 2 months ago
Shared Kernel Information Embedding for Discriminative Inference
Latent Variable Models (LVM), like the Shared-GPLVM and the Spectral Latent Variable Model, help mitigate over- fitting when learning discriminative methods from small or modera...
David J. Fleet, Leonid Sigal, Roland Memisevic
ICCS
2007
Springer
14 years 1 months ago
Learning Common Outcomes of Communicative Actions Represented by Labeled Graphs
We build a generic methodology based on learning and reasoning to detect specific attitudes of human agents and patterns of their interactions. Human attitudes are determined in te...
Boris Galitsky, Boris Kovalerchuk, Sergei O. Kuzne...
ICML
2009
IEEE
14 years 8 months ago
Partially supervised feature selection with regularized linear models
This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be availa...
Thibault Helleputte, Pierre Dupont
PKDD
2010
Springer
128views Data Mining» more  PKDD 2010»
13 years 5 months ago
Learning to Tag from Open Vocabulary Labels
Most approaches to classifying media content assume a fixed, closed vocabulary of labels. In contrast, we advocate machine learning approaches which take advantage of the millions...
Edith Law, Burr Settles, Tom M. Mitchell
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
14 years 8 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy