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Vapnik–Chervonenkis theory (also known as VC theory) was developed during 1960–1990 by Vladimir Vapnik and Alexey Chervonenkis. The theory is a form of computational learning theory, which attempts to explain the learning process from a statistical point of view. VC theory is related to statistical learning theory and to empirical processes. Richard M. Dudley and Vladimir Vapnik, among others, have applied VC-theory to empirical processes. == Introduction == VC theory covers at least four parts (as explained in ''The Nature of Statistical Learning Theory''): *Theory of consistency of learning processes * *What are (necessary and sufficient) conditions for consistency of a learning process based on the empirical risk minimization principle? *Nonasymptotic theory of the rate of convergence of learning processes * *How fast is the rate of convergence of the learning process? *Theory of controlling the generalization ability of learning processes * *How can one control the rate of convergence (the generalization ability) of the learning process? *Theory of constructing learning machines * *How can one construct algorithms that can control the generalization ability? VC Theory is a major subbranch of statistical learning theory. One of its main applications in statistical learning theory is to provide generalization conditions for learning algorithms. From this point of view, VC theory is related to stability, which is an alternative approach for characterizing generalization. In addition, VC theory and VC dimension are instrumental in the theory of empirical processes, in the case of processes indexed by VC classes. Arguably these are the most important applications of the VC theory, and are employed in proving generalization. Several techniques will be introduced that are widely used in the empirical process and VC theory. The discussion is mainly based on the book "Weak Convergence and Empirical Processes: With Applications to Statistics". 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「Vapnik–Chervonenkis theory」の詳細全文を読む スポンサード リンク
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