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DAGM
2004
Springer
14 years 1 months ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf
ICASSP
2008
IEEE
14 years 2 months ago
Learning with noisy supervision for Spoken Language Understanding
Data-driven Spoken Language Understanding (SLU) systems need semantically annotated data which are expensive, time consuming and prone to human errors. Active learning has been su...
Christian Raymond, G. Riccardfi
WACV
2005
IEEE
14 years 1 months ago
Semi-Supervised Self-Training of Object Detection Models
The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled i...
Chuck Rosenberg, Martial Hebert, Henry Schneiderma...
CORR
1999
Springer
118views Education» more  CORR 1999»
13 years 7 months ago
Supervised Grammar Induction Using Training Data with Limited Constituent Information
Corpus-based grammar induction generally relies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of building large annotated corpora is...
Rebecca Hwa
ICML
2003
IEEE
14 years 8 months ago
Semi-Supervised Learning of Mixture Models
This paper analyzes the performance of semisupervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations wh...
Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar C...