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AAAI
2011
12 years 7 months ago
Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been fou...
Yu-Feng Li, Zhi-Hua Zhou
ICASSP
2011
IEEE
12 years 11 months ago
Exploiting active-learning strategies for annotating prosodic events with limited labeled data
Many applications of spoken-language systems can benefit from having access to annotations of prosodic events. Unfortunately, obtaining human annotations of these events, even se...
Raul Fernandez, Bhuvana Ramabhadran
JMLR
2006
186views more  JMLR 2006»
13 years 7 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
IJON
2010
152views more  IJON 2010»
13 years 6 months ago
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data
Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensiona...
Kerstin Bunte, Barbara Hammer, Axel Wismüller...
ACL
2009
13 years 5 months ago
Unsupervised Argument Identification for Semantic Role Labeling
The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identification, and argument classification. Current SRL algorithms show lower results ...
Omri Abend, Roi Reichart, Ari Rappoport