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ALT

2009

Springer

2009

Springer

Abstract. We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which diﬀerent queries have diﬀerent costs. Moreover, queries may have more than two possible responses and the distribution over hypotheses may be non uniform. Speciﬁc applications include active learning with label costs, active learning for multiclass and partial label queries, and batch mode active learning. We also discuss an approximate version of interest when there are very many queries. 1 Motivation We ﬁrst motivate the problem by describing it informally. Imagine two people are playing a variation of twenty questions. Player 1 selects an object from a ﬁnite set, and it is up to player 2 to identify the selected object by asking questions chosen from a ﬁnite set. We assume for every object and every question the answer is unambiguous: each question maps each object to a single answer. Furthermore, each question has associated...

Related Content

Added |
14 Mar 2010 |

Updated |
14 Mar 2010 |

Type |
Conference |

Year |
2009 |

Where |
ALT |

Authors |
Andrew Guillory, Jeff A. Bilmes |

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