Learning Ensembles of Structured Prediction Rules

被引：0

作者：

Cortes, Corinna ^{[1
]}

Kuznetsov, Vitaly ^{[2
]}

Mohri, Mehryar ^{[2
,3
]}

机构：

[1] Google Res, 111 8th Ave, New York, NY 10011 USA

[2] Courant Inst, New York, NY 10012 USA

[3] Google Res, New York, NY 10012 USA

来源：

PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 | 2014年

基金：

加拿大自然科学与工程研究理事会;

关键词：

ALGORITHMS; LANGUAGE; SYSTEM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a series of algorithms with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed. This includes a number of randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones, and a boosting-style algorithm applicable in the context of structured prediction with a large number of labels. We also report the results of extensive experiments with these algorithms.

引用

页码：1 / 12

页数：12

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