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Algorithmic learning theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and algorithmic inductive inference. Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis. Both algorithmic and statistical learning theory are concerned with machine learning and can thus be viewed as branches of computational learning theory. ==Distinguishing Characteristics== Unlike statistical learning theory and most statistical theory in general, algorithmic learning theory does not assume that data are random samples, that is, that data points are independent of each other. This makes the theory suitable for domains where observations are (relatively) noise-free but not random, such as language learning 〔Jain, S. et al (1999): ''Systems That Learn'', 2nd ed. Cambridge, MA: MIT Press.〕 and automated scientific discovery.〔Langley, P.; Simon, H.; Bradshaw, G. & Zytkow, J. (1987), ''Scientific Discovery: Computational Explorations of the Creative Processes'', MIT Press, Cambridge〕〔Schulte, O. (2009), ''Simultaneous Discovery of Conservation Laws and Hidden Particles With Smith Matrix Decomposition'', in Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 1481-1487〕 The fundamental concept of algorithmic learning theory is learning in the limit: as the number of data points increases, a learning algorithm should converge to a correct hypothesis on ''every'' possible data sequence consistent with the problem space. This is a non-probabilistic version of statistical consistency, which also requires convergence to a correct model in the limit, but allows a learner to fail on data sequences with probability measure 0. Algorithmic learning theory investigates the learning power of Turing machines. Other frameworks consider a much more restricted class of learning algorithms than Turing machines, for example learners that compute hypotheses more quickly, for instance in polynomial time. An example of such a framework is probably approximately correct learning. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Algorithmic learning theory」の詳細全文を読む スポンサード リンク
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