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DAGM
2004
Springer
16 years 3 days 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
157
Voted
PKDD
2010
Springer
128views Data Mining» more  PKDD 2010»
15 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
ICALT
2006
IEEE
16 years 23 days ago
Instruction Through The Ages: Building Pervasive Virtual Instructors for Life Long Learning
A pervasive virtual instructor is an artificially intelligent instructor that may appear transparent to the learner or appear in the form of a threedimensional graphical character...
Jayfus T. Doswell
ICRA
2007
IEEE
189views Robotics» more  ICRA 2007»
16 years 1 months ago
Context Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contexts
— Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it’s use for robust pre...
Georgios Petkos, Sethu Vijayakumar
ECML
2005
Springer
16 years 7 days ago
Learning from Positive and Unlabeled Examples with Different Data Distributions
Abstract. We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive exam...
Xiaoli Li, Bing Liu