Supervised Learning

• Published in: Machine Learning Techniques for Multimedia pp. 21 - 49
• Main Authors: Cunningham, Pádraig, Cord, Matthieu, Delany, Sarah Jane
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Cognitive Technologies
• Subjects: Competence Model; Ensemble Member; Neural Network Ensemble; Random Forest; Support Vector Machine
• ISBN: 9783540751700; 354075170X
• ISSN: 1611-2482
• DOI: 10.1007/978-3-540-75171-7_2

Supervised learning accounts for a lot of research activity in machine learning and many supervised learning techniques have found application in the processing of multimedia content. The defining characteristic of supervised learning is the availability of annotated training data. The name invokes the idea of a ‘supervisor’ that instructs the learning system on the labels to associate with training examples. Typically these labels are class labels in classification problems. Supervised learning algorithms induce models from these training data and these models can be used to classify other unlabelled data. In this chapter we ground or analysis of supervised learning on the theory of risk minimization. We provide an overview of support vector machines and nearest neighbour classifiers~– probably the two most popular supervised learning techniques employed in multimedia research.