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The term Active Learning is generally used to refer to a learning problem or system where the learner has some role in determining on what data it will be trained. This is in contrast to Passive Learning, where the learner is simply presented with a training set over which it has no control. Active learning is often used in settings where obtaining labeled data is expensive or time-consuming; by sequentially identifying which examples are most likely to be useful, an active learner can sometimes achieve good performance, using far less training data than would otherwise be required.
Structure of Learning System
In many machine learning problems, the training data are treated as a fixed and given part of the problem definition. In practice, however, the training data are often not fixed beforehand. Rather, the learner has an opportunity to play a role in deciding what data will be acquired for training. This process is usually referred to as “active learning,” recognizing...
- Cohn D, Atlas L, Ladner R (1990) Training connectionist networks with queries and selective sampling. In: Touretzky D (ed) Advances in neural information processing systems. Morgan Kaufmann, San MateoGoogle Scholar
- Cohn D, Ghahramani Z, Jordan MI (1996) Active learning with statistical models. J Artif Intell Res 4:129–145. http://citeseer.ist.psu.edu/321503.html
- Dasgupta S (1999) Learning mixtures of Gaussians. Found Comput Sci 634–644Google Scholar
- Fedorov V (1972) Theory of optimal experiments. Academic Press, New YorkGoogle Scholar
- Kearns M, Li M, Pitt L, Valiant L (1987) On the learnability of Boolean formulae. In: Proceedings of the 19th annual ACM conference on theory of computing. ACM Press, New York, pp 285–295Google Scholar
- Lewis DD, Gail WA (1994) A sequential algorithm for training text classifiers. In: Proceedings of the 17th annual international ACM SIGIR conference, Dublin, pp 3–12Google Scholar
- McCallum A, Nigam K (1998) Employing EM and pool-based active learning for text classification. In: Machine learning: proceedings of the fifteenth international conference (ICML’98), Madison, pp 359–367Google Scholar
- North DW (1968) A tutorial introduction to decision theory. IEEE Trans Syst Sci Cybern 4(3)Google Scholar
- Ruff R, Dietterich T (1989) What good are experiments? In: Proceedings of the sixth international workshop on machine learning, IthacaGoogle Scholar
- Seung HS, Opper M, Sompolinsky H (1992) Query by committee. In: Proceedings of the fifth workshop on computational learning theory. Morgan Kaufmann, San Mateo, pp 287–294Google Scholar
- Steck H, Jaakkola T (2002) Unsupervised active learning in large domains. In: Proceeding of the conference on uncertainty in AI. http://citeseer.ist.psu.edu/steck02unsupervised.html