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Learning based on Similarity

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Book cover Knowledge Transfer between Computer Vision and Text Mining

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

This chapter describes all the machine learning methods that are employed in this book to obtain results for different applications of computer vision and string processing . The chapter gives an overview of the main concepts of learning based on similarity. Specific machine learning methods that are based on these concepts are then presented. First, nearest neighbor models are discussed. A nonstandard learning formulation based on the notions of similarity and nearest neighbors , known as local learning , is then presented. An overview of kernel methods is also given, since the state-of-the-art methods consistently used in the supervised learning tasks presented throughout this book are kernel methods . This chapter ends with a discussion about cluster analysis . Clustering techniques are used throughout this book in various contexts, from building vocabularies of visual words to phylogenetic analysis .

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Authors and Affiliations

  1. Department of Computer Science, University of Bucharest, Bucharest, Romania

    Radu Tudor Ionescu & Marius Popescu

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  1. Radu Tudor Ionescu
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  2. Marius Popescu
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Correspondence to Radu Tudor Ionescu .

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Ionescu, R.T., Popescu, M. (2016). Learning based on Similarity. In: Knowledge Transfer between Computer Vision and Text Mining. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-30367-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-30367-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30365-9

  • Online ISBN: 978-3-319-30367-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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