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CVPR
2012
IEEE
7 years 1 months ago
RALF: A reinforced active learning formulation for object class recognition
Active learning aims to reduce the amount of labels required for classification. The main difficulty is to find a good trade-off between exploration and exploitation of the lab...
Sandra Ebert, Mario Fritz, Bernt Schiele
CVPR
2012
IEEE
7 years 4 months ago
Stream-based Joint Exploration-Exploitation Active Learning
Learning from streams of evolving and unbounded data is an important problem, for example in visual surveillance or internet scale data. For such large and evolving real-world data...
Chen Change Loy, Timothy M. Hospedales, Tao Xiang,...
ACL
2011
8 years 2 months ago
Pointwise Prediction for Robust, Adaptable Japanese Morphological Analysis
We present a pointwise approach to Japanese morphological analysis (MA) that ignores structure information during learning and tagging. Despite the lack of structure, it is able t...
Graham Neubig, Yosuke Nakata, Shinsuke Mori
PAKDD
2011
ACM
473views Data Mining» more  PAKDD 2011»
8 years 5 months ago
 Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning
Discovering rare categories and classifying new instances of them is an important data mining issue in many fields, but fully supervised learning of a rare class classifier is pr...
Timothy Hospedales, Shaogang Gong and Tao Xiang
SIGKDD
2010
183views more  SIGKDD 2010»
8 years 6 months ago
Inactive learning?: difficulties employing active learning in practice
Despite the tremendous level of adoption of machine learning techniques in real-world settings, and the large volume of research on active learning, active learning techniques hav...
Josh Attenberg, Foster J. Provost
JMLR
2010
162views more  JMLR 2010»
8 years 6 months ago
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...
INTERSPEECH
2010
8 years 6 months ago
Memory-based active learning for French broadcast news
Stochastic dependency parsers can achieve very good results when they are trained on large corpora that have been manually annotated. Active learning is a procedure that aims at r...
Frédéric Tantini, Christophe Cerisar...
IJCV
2011
264views more  IJCV 2011»
8 years 6 months ago
Cost-Sensitive Active Visual Category Learning
Abstract We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking un...
Sudheendra Vijayanarasimhan, Kristen Grauman
COLING
2010
8 years 6 months ago
Active Deep Networks for Semi-Supervised Sentiment Classification
This paper presents a novel semisupervised learning algorithm called Active Deep Networks (ADN), to address the semi-supervised sentiment classification problem with active learni...
Shusen Zhou, Qingcai Chen, Xiaolong Wang
COLING
2010
8 years 6 months ago
Bringing Active Learning to Life
Active learning has been applied to different NLP tasks, with the aim of limiting the amount of time and cost for human annotation. Most studies on active learning have only simul...
Ines Rehbein, Josef Ruppenhofer, Alexis Palmer
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