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ALT

2004

Springer

2004

Springer

Abstract. We present a survey of recent results concerning the theoretical and empirical performance of algorithms for learning regularized least-squares classifiers. The behavior of these family of learning algorithms is analyzed in both the statistical and the worst-case (individual sequence) data-generating models. 1 Regularized Least-Squares for Classification In the pattern classification problem, some unknown source is supposed to generate a sequence x1, x2, . . . of instances (data elements) xt X, where X is usually taken to be Rd for some fixed d. Each instance xt is associated with a class label yt Y, where Y is a finite set of classes, indicating a certain semantic property of the instance. For instance, in a handwritten digit recognition task, xt is the digitalized image of a handwritten digit and its label yt {0, 1, . . . , 9} is the corresponding numeral. A learning algorithm for pattern classification uses a set of training examples, that is pairs (xt, yt), to build a ...

Related Content

Added |
20 Aug 2010 |

Updated |
20 Aug 2010 |

Type |
Conference |

Year |
2004 |

Where |
ALT |

Authors |
Nicolò Cesa-Bianchi |

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