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. 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, in which each dimension represents the value of one of the pixels.
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. Classification functions can use the percentage of inputs that are correctly classified as a metric for how predictive the learned function is, while regression functions must use some distance metric, called a loss function, for how accurate the predicted value is. A familiar example of a loss function is the square of the difference between the actual value and the predicted value; this is the loss function used in ordinary least squares regression.
Read more about this topic: Statistical Learning Theory
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