Original sentence: Nonasymptotic theory of the rate of convergence of learning processes.Reproducing kernel Hilbert spaces.Statistical Learning Theory, Vladimir Vapnik, Wiley-Interscience, (1998), ISBN 0471030031The Statistical learning theory is a theory developed during 1960-1990 mainly by Vladimir Vapnik and Alexey Chervonenkis.The Nature of Statistical Learning Theory, Vladimir Vapnik, Springer-Verlag, (1999), ISBN 0387987800.The Statistical learning theory contains important concepts such as the VC dimension and Structural risk minimization.The last part of the theory introduced an well-known learning algorithm: the Support vector machine.The theory explains the learning process from a statistical point of view.Theory of consistency of learning processes.Theory of constructing learning machines.Theory of controlling the generalization ability of learning processes.This foundational theory unifies such disparate algorithms such as neural networks, principal component analysis, and maximum likelihood.This theory is foundation of a real understanding of machine learning.This theory is related to mathematical subjects such as:.What are (necessary and sufficient) conditions for consistency of a learning process based on the Empirical Risk Minimization principle
Edited sentence: Nonasymptotic theory of the rate of convergence of learning processes, or.Statistical Learning Theory (the whole mathematics).The Nature of Statistical Learning Theory (a must).The Statistical learning theory introduced important concepts such as the VC dimension, the Structural Risk Minimization, etc....The Statistical learning theory is a theory develloped during 1960-1990 mainly by Vladimir Vapnik and Alexey Chervonenkis that explain the learning process from a statistical point of view.The last part of the theory introduced an actually very used learning machine the Support Vector Machine.Theory of consistency of learning processes, or.Theory of constructing learning machines, or.Theory of controlling the generalization ability of learning processes, or.This fundational theory explain the indepth of until disparate ideas such as Neural Networks, PCA, ML, MLD, etc....This theory is based/inspired/related to mathematical subjects such as:.This theory is foundation of a real understanding of the machines learning abilities and support most of the greatest engineering application of the 21th century.What are (necesarry and sufficient) conditions for consistency of a learning process based on the Empirical Risk Minimization principle