CIKM
2008
Springer
13 years 6 months ago
2008
Springer
Proactive learning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications. Active learning seeks to select the m...
KDD
2010
ACM
13 years 6 months ago
2010
ACM
Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe ...
AAAI
2008
13 years 6 months ago
2008
For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, c...
ECML
2006
Springer
13 years 8 months ago
2006
Springer
In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
ECML
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there ma...
ECML
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. We propose a novel active learning strategy based on the compression framework of [9] for label ranking functions which, given an input instance, predict a total order ov...
CIVR
2006
Springer
13 years 8 months ago
2006
Springer
Aspect-based relevance learning is a relevance feedback scheme based on a natural model of relevance in terms of image aspects. In this paper we propose a number of active learning...
CIKM
2006
Springer
13 years 8 months ago
2006
Springer
In practical classification, there is often a mix of learnable and unlearnable classes and only a classifier above a minimum performance threshold can be deployed. This problem is...
CVPR
2007
IEEE
13 years 8 months ago
2007
IEEE
Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recen...
CEAS
2007
Springer
13 years 8 months ago
2007
Springer
Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustwo...