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Active learning (machine learning)

Known as: Pool-based active learning 
Active learning is a special case of semi-supervised machine learning in which a learning algorithm is able to interactively query the user (or some… Expand
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Bayesian experimental designCold startEvolving intelligent systemList of datasets for machine learning research
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Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Deep Bayesian Active Learning with Image Data
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep… Expand
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Review
2017
Review
2017
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in… Expand
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Review
2013
Review
2013
Machine Learning Paradigms for Speech Recognition: An Overview
  • L. Deng, Xiao Li
  • IEEE Transactions on Audio, Speech, and Language…
  • 2013
    • Corpus ID: 16585863
Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including… Expand
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Review
2012
Review
2012
Active Learning
The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed… Expand
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Highly Cited
2010
Highly Cited
2010
Active Learning
  • David Cohn
  • Encyclopedia of Machine Learning
  • 2010
    • Corpus ID: 30334806
Active learning is generally defined as any instructional method that engages students in the learning process. In short, active… Expand
Review
2009
Review
2009
Dataset Shift in Machine Learning
Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs… Expand
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Review
2008
Review
2008
A machine learning approach to sentiment analysis in multilingual Web texts
Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial… Expand
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Highly Cited
2007
Highly Cited
2007
Learning on the border: active learning in imbalanced data classification
This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of… Expand
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Highly Cited
2004
Highly Cited
2004
Improving generalization with active learning
Active learning differs from “learning from examples” in that the learning algorithm assumes at least some control over what part… Expand
Highly Cited
2001
Highly Cited
2001
Support Vector Machine Active Learning with Applications to Text Classification
Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine… Expand
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