Supervised Learning
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...
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| Published in | Machine Learning Techniques for Multimedia pp. 21 - 49 |
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| Main Authors | , , |
| Format | Book Chapter |
| Language | English |
| Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
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| Series | Cognitive Technologies |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Cord, Matthieu Delany, Sarah Jane Cunningham, Pádraig |
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| Copyright | Springer-Verlag Berlin Heidelberg 2008 |
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| DOI | 10.1007/978-3-540-75171-7_2 |
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| EISBN | 3540751718 9783540751717 |
| Editor | Cord, Matthieu Cunningham, Pádraig |
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| SubjectTerms | Competence Model Ensemble Member Neural Network Ensemble Random Forest Support Vector Machine |
| Title | Supervised Learning |
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