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ICML
2005
IEEE
14 years 9 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
CVPR
2003
IEEE
14 years 10 months ago
Bootstrapping SVM Active Learning by Incorporating Unlabelled Images for Image Retrieval
The performance of image retrieval with SVM active learning is known to be poor when started with few labelled images only. In this paper, the problem is solved by incorporating t...
Lei Wang, Kap Luk Chan, Zhihua Zhang
ICML
2004
IEEE
14 years 2 months ago
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
Hisashi Kashima, Yuta Tsuboi
CIVR
2006
Springer
186views Image Analysis» more  CIVR 2006»
14 years 14 days ago
Leveraging Active Learning for Relevance Feedback Using an Information Theoretic Diversity Measure
Abstract. Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more recently active learning are two standard ...
Charlie K. Dagli, ShyamSundar Rajaram, Thomas S. H...
ACL
2012
11 years 11 months ago
Labeling Documents with Timestamps: Learning from their Time Expressions
Temporal reasoners for document understanding typically assume that a document’s creation date is known. Algorithms to ground relative time expressions and order events often re...
Nathanael Chambers