Active Learning Algorithm
An active learning algorithm is an iterative supervised learning algorithm that can be implemented by an active learning system (to solve an active learning task which that allows more than one round of labeling).
- Context:
- It can range from being a Fully-Supervised Active Learning Algorithm to being a Semi-Supervised Active Learning Algorithm.
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- Example(s):
- Counter-Example(s):
- See: Machine Learning, Reinforcement Learning, Conformal Predictor, Online Machine Learning, Optimal Experimental Design, Incremental Learning.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Active_learning_(machine_learning) Retrieved:2020-9-15.
- Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called teacher or oracle.
There are situations in which unlabeled data is abundant but manual 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 is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, 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.
- Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called teacher or oracle.
2013
- (Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/active_learning Retrieved:2013-12-4.
- Active learning is an umbrella term that refers to several models of instruction that focus the responsibility of learning on learners. Bonwell and Eison (1991) popularized this approach to instruction . This buzzword of the 1980s became their 1990s report to the Association for the Study of Higher Education (ASHE). In this report they discuss a variety of methodologies for promoting "active learning". They cite literature which indicates that to learn, students must do more than just listen: They must read, write, discuss, or be engaged in solving problems. It relates to the three learning domains referred to as knowledge, skills and attitudes (KSA), and that this taxonomy of learning behaviours can be thought of as “the goals of the learning process”(Bloom, 1956). In particular, students must engage in such higher-order thinking tasks as analysis, synthesis, and evaluation. [1]
Active learning engages students in two aspects – doing things and thinking about the things they are doing (Bonwell and Eison, 1991).
- Active learning is an umbrella term that refers to several models of instruction that focus the responsibility of learning on learners. Bonwell and Eison (1991) popularized this approach to instruction . This buzzword of the 1980s became their 1990s report to the Association for the Study of Higher Education (ASHE). In this report they discuss a variety of methodologies for promoting "active learning". They cite literature which indicates that to learn, students must do more than just listen: They must read, write, discuss, or be engaged in solving problems. It relates to the three learning domains referred to as knowledge, skills and attitudes (KSA), and that this taxonomy of learning behaviours can be thought of as “the goals of the learning process”(Bloom, 1956). In particular, students must engage in such higher-order thinking tasks as analysis, synthesis, and evaluation. [1]
2009
- (Settles, 2009) ⇒ Burr Settles. (2008). “Active Learning Literature Survey.” Computer Sciences Technical Report 1648, University of Wisconsin-Madison. 2009.
- An introduction to active learning and a survey of the literature. This paper outlines the various learning scenarios, query strategy frameworks, variants, application domains, and related work published over the past few decades.
2008
- (Settles, 2008) ⇒ Burr Settles. (2008). “Curious Machines: Active Learning with Structured Instances.” PhD.
2001
- (Tang et al., 2001) ⇒ Min Tang, Xiaoqiang Luo, and Salim Roukos. (2001). “Active Learning for Statistical Natural Language Parsing.” In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL 2001).
1997
- (Freund et al., 1997) ⇒ Yoav Freund, Sebastian H. Seung, Eli Shamir, and Naftali Tishby (1997). “Selective Sampling Using the Query by Committee Algorithm.” In: Machine Learning, 28.
1994
- (Cohn et al., 1994) ⇒ David Cohn, Les Atlas, and Richard Ladner. (1994). “Improving Generalization with Active Learning.” In: Machine Learning, 15(2). doi:10.1023/A:1022673506211.