DOI: 10.1007/978-3-642-14267-3_22 Entity type: sg:BookChapter

2011

Machine Learning

In this chapter, we introduce some basics of machine learning. Specifically, we first give a brief introduction to regression and classification, which are typical machine learning tasks studied in the literature. To this end, we take linear regression, neural networks, support vector machines, Boosting, and K nearest neighbors as examples, introduce their basic ideas, and discuss their properties. Many of these algorithms have been extended to handle the problem of learning to rank. Then, we move onto the statistical learning theory, which is concerned with the theoretical guarantee of the generalization of a machine learning algorithm from the training set to the unseen test set. Familiarity with the notations and theorems mentioned in this chapter will be very helpful for the readers to understand different learning to rank algorithms and the statistical learning theory for ranking.

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25 TRIPLES 25 PREDICATES 22 URIs 14 LITERALS

Subject | Predicate | Object | |
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1 | book-chapters:e238ec89e0f3a6865ff6b43793237b04 | sg:abstract | Abstract In this chapter, we introduce some basics of machine learning. Specifically, we first give a brief introduction to regression and classification, which are typical machine learning tasks studied in the literature. To this end, we take linear regression, neural networks, support vector machines, Boosting, and K nearest neighbors as examples, introduce their basic ideas, and discuss their properties. Many of these algorithms have been extended to handle the problem of learning to rank. Then, we move onto the statistical learning theory, which is concerned with the theoretical guarantee of the generalization of a machine learning algorithm from the training set to the unseen test set. Familiarity with the notations and theorems mentioned in this chapter will be very helpful for the readers to understand different learning to rank algorithms and the statistical learning theory for ranking. |

2 | ″ | sg:abstractRights | OpenAccess |

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4 | ″ | sg:bodyHtmlRights | Restricted |

5 | ″ | sg:bodyPdfRights | Restricted |

6 | ″ | sg:chapterNumber | Chapter 22 |

7 | ″ | sg:copyrightHolder | Springer-Verlag Berlin Heidelberg |

8 | ″ | sg:copyrightYear | 2011 |

9 | ″ | sg:ddsId | Chap22 |

10 | ″ | sg:doi | 10.1007/978-3-642-14267-3_22 |

11 | ″ | sg:esmRights | OpenAccess |

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15 | ″ | sg:hasContribution | contributions:42514e22929ddc2f09ce45ba45b43bb2 |

16 | ″ | sg:language | En |

17 | ″ | sg:license | http://scigraph.springernature.com/explorer/license/ |

18 | ″ | sg:metadataRights | OpenAccess |

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21 | ″ | sg:scigraphId | e238ec89e0f3a6865ff6b43793237b04 |

22 | ″ | sg:title | Machine Learning |

23 | ″ | sg:webpage | https://link.springer.com/10.1007/978-3-642-14267-3_22 |

24 | ″ | rdf:type | sg:BookChapter |

25 | ″ | rdfs:label | BookChapter: Machine Learning |

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