Synonyms
Adaptive learning; Online learning; Transfer learning
Definition
Incremental learning is a machine learning paradigm where the learning process takes place whenever new example(s) emerges and adjusts what has been learned according to the new example(s). The most prominent difference of incremental learning from traditional machine learning is that it does not assume the availability of a sufficient training set before the learning process, but the training examples appear over time.
Introduction
For a long time in the history of machine learning, there has been an implicit assumption that a “good” training set in a domain is available a priori.The training set is so “good” that it contains all necessary knowledge that once learned and can be reliably applied to any new examples in the domain. Consequently, emphasis is put on learning as much as possible from a fixed training set. Unfortunately, many real-world applications cannot match this ideal case, such as in dynamic...
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References
C. G. Giraud-Carrier, A note on the utility of incremental learning. AI Commun. 13(4), 215–224 (2000)
D. Ourston, R. J. Mooney, Theory refinement combining analytical and empirical methods. Artif. Intell. 66(2), 273–309 (1994)
J. L. Elman, Learning and development in neural networks: the importance of starting small. Cogn. 46(1), 71–99 (1993)
L. Cheng, S. V. N. Vishwanathan, D. Schuurmans, S. Wang, T. Caelli, Implicit online learning with kernels, in Advances in Neural Information Processing Systems, vol. 19, Vancouver, 2006, pp. 249–256
Q. Huo, C. H. Lee, On-line adaptive learning of the continuous density hidden markov model based on approximate recursive bayes estimate. IEEE Trans. Speech Audio Process. 5(2), 161–172 (1997)
S. J. Pan, J. T. Kwok, Q. Yang, Transfer learning via dimensionality reduction, in Proceedings of the AAAI Conference on Artificial Intelligence, Chicago, 2008, pp. 677–682
T. M. Cover, P. E. Hart, Nearest neighbour pattern classification. Trans. Inf. Theory 13, 21–27 (1967)
J. C. Schlimmer, D. H. Fisher, A case study of incremental concept induction. in Proceedings of the National Conference on Artifical Intelligence, San Mateo, 1986, pp. 496–501
D. W. Aha, D. F. Kibler, M. K. Albert, Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)
N. A. Syed, H. Liu, K. K. Sung, Handling concept drifts in incremental learning with support vector machines, in Proceedings of ACM International Conference on Knowledge Discovery and Data Mining, San Diego, 1999, pp. 317–321
D. A. Ross, J. Lim, R. S. Lin, M. H. Yang, Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)
J. C. Schlimmer, R. H. Granger, Incremental learning from noisy data. Mach. Learn. 1(3), 317–354 (1986)
Z. H. Zhou, Z. Chen, Hybrid decision tree. Knowl. Based Syst. 15(8), 515–528 (2002)
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Geng, X., Smith-Miles, K. (2015). Incremental Learning. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_304
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DOI: https://doi.org/10.1007/978-1-4899-7488-4_304
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