|
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis.〔Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Foundations of Machine Learning'', The MIT Press ISBN 9780262018258.〕 Statistical learning theory deals with the problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, and bioinformatics. ==Introduction== The goal of learning is prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood.〔Tomaso Poggio, Lorenzo Rosasco, et al. ''Statistical Learning Theory and Applications'', 2012, Class 1 ()〕 Supervised learning involves learning from a training set of data. Every point in the training is an input-output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output in a predictive fashion, such that the learned function can be used to predict output from future input. Depending of the type of output, supervised learning problems are either problems of regression or problems of classification. If the output takes a continuous range of values, it is a regression problem. Using Ohm's Law as an example, a regression could be performed with voltage as input and current as output. The regression would find the functional relationship between voltage and current to be , such that : Classification problems are those for which the output will be an element from a discrete set of labels. Classification is very common for machine learning applications. In facial recognition, for instance, a picture of a person's face would be the input, and the output label would be that person's name. The input would be represented by a large multidimensional vector whose elements represent pixels in the picture. After learning a function based on the training set data, that function is validated on a test set of data, data that did not appear in the training set. 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「Statistical learning theory」の詳細全文を読む スポンサード リンク
|