COPYRIGHT YEAR

2016

AUTHORS

Thiago Christiano Silva, Liang Zhao

TITLE

Machine Learning

ABSTRACT

Machine learning relates to the study, design and development of algorithms that give computers the capability to learn without being explicitly programmed. Machine learning techniques are fairly generic and can be applied in various settings. To utilize such kinds of algorithms, one has to translate the problem to the domain of machine learning, which usually expects a set of features and a desirable output or grouping criterion. In this chapter, we introduce the three machine learning paradigms often employed by the literature: supervised, unsupervised, and semi-supervised. We show that supervised algorithms exclusively utilize external information to induce or to train their hypotheses. In contrast, unsupervised learning methods are guided exclusively by the intrinsic structure of the data items throughout the learning process, i.e., without any sort of external knowledge. In-between these two learning paradigms lies the semi-supervised learning, which employs both the labeled and unlabeled data in the learning process. Here, we focus on supplying the shortcomings and potentialities of traditional and representative techniques that are well-known by the machine learning community. We will not go into technical details of traditional machine learning techniques in this chapter, because these are not the focus of this book.

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21 TRIPLES      19 PREDICATES      22 URIs      12 LITERALS

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1 book-chapters:7baa96b8a970dcf601901055c74ea905 sg:abstract Abstract Machine learning relates to the study, design and development of algorithms that give computers the capability to learn without being explicitly programmed. Machine learning techniques are fairly generic and can be applied in various settings. To utilize such kinds of algorithms, one has to translate the problem to the domain of machine learning, which usually expects a set of features and a desirable output or grouping criterion. In this chapter, we introduce the three machine learning paradigms often employed by the literature: supervised, unsupervised, and semi-supervised. We show that supervised algorithms exclusively utilize external information to induce or to train their hypotheses. In contrast, unsupervised learning methods are guided exclusively by the intrinsic structure of the data items throughout the learning process, i.e., without any sort of external knowledge. In-between these two learning paradigms lies the semi-supervised learning, which employs both the labeled and unlabeled data in the learning process. Here, we focus on supplying the shortcomings and potentialities of traditional and representative techniques that are well-known by the machine learning community. We will not go into technical details of traditional machine learning techniques in this chapter, because these are not the focus of this book.
2 sg:chapterNumber Chapter 3
3 sg:copyrightHolder Springer International Publishing Switzerland
4 sg:copyrightYear 2016
5 sg:ddsId b978-3-319-17290-3_3
6 sg:doi 10.1007/978-3-319-17290-3_3
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12 contributions:57d22dfb5e84d65cd3c7bd344a20f74a
13 sg:language En
14 sg:license http://scigraph.springernature.com/explorer/license/
15 sg:pageFirst 71
16 sg:pageLast 91
17 sg:scigraphId 7baa96b8a970dcf601901055c74ea905
18 sg:title Machine Learning
19 sg:webpage https://link.springer.com/10.1007/978-3-319-17290-3_3
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21 rdfs:label BookChapter: Machine Learning
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