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TASLP
2010
144views more  TASLP 2010»
8 years 5 months ago
Active Learning With Sampling by Uncertainty and Density for Data Annotations
To solve the knowledge bottleneck problem, active learning has been widely used for its ability to automatically select the most informative unlabeled examples for human annotation...
Jingbo Zhu, Huizhen Wang, Benjamin K. Tsou, Matthe...
NIPS
2000
8 years 11 months ago
Kernel Expansions with Unlabeled Examples
Modern classification applications necessitate supplementing the few available labeled examples with unlabeled examples to improve classification performance. We present a new tra...
Martin Szummer, Tommi Jaakkola
IJCAI
2003
8 years 11 months ago
Integrating Background Knowledge Into Text Classification
We present a description of three different algorithms that use background knowledge to improve text classifiers. One uses the background knowledge as an index into the set of tra...
Sarah Zelikovitz, Haym Hirsh
NIPS
2001
8 years 11 months ago
Active Learning in the Drug Discovery Process
We investigate the following data mining problem from Computational Chemistry: From a large data set of compounds, find those that bind to a target molecule in as few iterations o...
Manfred K. Warmuth, Gunnar Rätsch, Michael Ma...
NIPS
2001
8 years 11 months ago
Partially labeled classification with Markov random walks
To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov random walk representation over the unlabeled examples. The random w...
Martin Szummer, Tommi Jaakkola
CIVR
2006
Springer
186views Image Analysis» more  CIVR 2006»
9 years 2 months 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...
WAIM
2010
Springer
9 years 3 months ago
Semi-supervised Learning from Only Positive and Unlabeled Data Using Entropy
Abstract. The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very sma...
Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester...
MIR
2003
ACM
178views Multimedia» more  MIR 2003»
9 years 3 months ago
A bootstrapping approach to annotating large image collection
Huge amount of manual efforts are required to annotate large image/video archives with text annotations. Several recent works attempted to automate this task by employing supervis...
HuaMin Feng, Tat-Seng Chua
PAKDD
2005
ACM
132views Data Mining» more  PAKDD 2005»
9 years 3 months ago
SETRED: Self-training with Editing
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the s...
Ming Li, Zhi-Hua Zhou
ECML
2005
Springer
9 years 3 months 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
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