Supervised Learning

Abstract

The problem of learning has two versions: supervised and unsupervised, that is with or without knowing the true class/parameter of the sample. This chapter addresses the first version. The purpose of learning (or training) is to use the measurement vectors of the samples to build a classifier or an estimator. The boosting algorithm offers an indirect way of converting a weak classifier to a strong one. The chapter discusses two approaches to supervised learning: parametric and non-parametric learning. The basic assumption in parametric learning is that the only unknown factors are parameters of the probability densities involved. Thus, learning from samples boils down to finding the suitable values of these parameters. Non-parametric methods are learning methods for which prior knowledge about the functional form of the conditional probability distributions is not available or is not used explicitly.

Cite this paper

@misc{anon2017supervised,
  title = {{Supervised Learning}},
  pages = {207--258},
  year = {2017},
  doi = {10.1002/9781119152484.ch6}
}

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