Active learning (machine learning)

This article is about a machine learning method. For active learning in the context of education, see 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 other information source) to obtain the desired outputs at new data points.[1] [2] In statistics literature it is sometimes also called optimal experimental design. [3]

There are situations in which unlabeled data is abundant but manually labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm be overwhelmed by uninformative examples. Recent developments are dedicated to hybrid active learning[4] and active learning in a single-pass (on-line) context,[5] combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning.

Definitions

Let be the total set of all data under consideration. For example, in a protein engineering problem, would include all proteins that are known to have a certain interesting activity and all additional proteins that one might want to test for that activity.

During each iteration, , is broken up into three subsets

  1. : Data points where the label is known.
  2. : Data points where the label is unknown.
  3. : A subset of that is chosen to be labeled.

Most of the current research in active learning involves the best method to choose the data points for .

Query strategies

Algorithms for determining which data points should be labeled can be organized into a number of different categories:[1]

A wide variety of algorithms have been studied that fall into these categories.[1][3]

Minimum Marginal Hyperplane

Some active learning algorithms are built upon Support vector machines (SVMs) and exploit the structure of the SVM to determine which data points to label. Such methods usually calculate the margin, , of each unlabeled datum in and treat as an -dimensional distance from that datum to the separating hyperplane.

Minimum Marginal Hyperplane methods assume that the data with the smallest are those that the SVM is most uncertain about and therefore should be placed in to be labeled. Other similar methods, such as Maximum Marginal Hyperplane, choose data with the largest . Tradeoff methods choose a mix of the smallest and largest s.

Meetings

See also

Notes

  1. 1 2 3 Settles, Burr (2010), "Active Learning Literature Survey" (PDF), Computer Sciences Technical Report 1648. University of Wisconsin–Madison, retrieved 2014-11-18
  2. Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016). "Active Learning in Recommender Systems". In Ricci, Francesco; Rokach, Lior; Shapira, Bracha. Recommender Systems Handbook (2 ed.). Springer US. ISBN 978-1-4899-7637-6.
  3. 1 2 Olsson, Fredrik. "A literature survey of active machine learning in the context of natural language processing".
  4. E. Lughofer (2012), Hybrid Active Learning (HAL) for Reducing the Annotation Efforts of Operators in Classification Systems. Pattern Recognition, vol. 45 (2), pp. 884-896, 2012.
  5. E. Lughofer (2012), Single-Pass Active Learning with Conflict and Ignorance. Evolving Systems, vol. 3 (4), pp. 251-271, 2012.
  6. Bouneffouf et .al (2014), Contextual Bandit for Active Learning: Active Thompson Sampling. Neural Information Processing - 21st International Conference, ICONIP 2014
  7. Bouneffouf et .al (2016), Exponentiated Gradient Exploration for Active Learning. Computers, vol. 5 (1), 2016, pp. 1-12
  8. http://vincentlemaire-labs.fr/iknow2016/

Other references

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