The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements—also called features or inputs—to the so-called class label—or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to approach a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.
Original languageEnglish
Title of host publicationMachine Learning Techniques for Space Weather
EditorsE. Camporeale, S. Wing, J.R. Johnson
PublisherElsevier
Chapter5
Pages113-145
Number of pages33
ISBN (Print)978-0-12-811788-0
DOIs
Publication statusPublished - 2018

    Research areas

  • Supervised learning, Pattern recognition, Machine learning, Representation, Classification, Evaluation

ID: 47688426