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Title:Selective algorithms for large-scale classification and structured learning
Author(s):Chang, Kai-Wei
Director of Research:Roth, Dan
Doctoral Committee Chair(s):Roth, Dan
Doctoral Committee Member(s):Forsyth, David A.; Zhai, ChengXiang; Platt, John
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Selective Learning Algorithm
Machine Learning
Structured Learning
Large-Scale Learning
Abstract:The desired output in many machine learning tasks is a structured object, such as tree, clustering, or sequence. Learning accurate prediction models for such problems requires training on large amounts of data, making use of expressive features and performing global inference that simultaneously assigns values to all interrelated nodes in the structure. All these contribute to significant scalability problems. In this thesis, we describe a collection of results that address several aspects of these problems – by carefully selecting and caching samples, structures, or latent items. Our results lead to efficient learning algorithms for large-scale binary classification models, structured prediction models and for online clustering models which, in turn, support reduction in problem size, improvements in training and evaluation speed and improved performance. We have used our algorithms to learn expressive models from large amounts of annotated data and achieve state-of-the art performance on several natural language processing tasks.
Issue Date:2015-04-23
Rights Information:Copyright 2015 Kai-Wei Chang
Date Available in IDEALS:2015-07-22
Date Deposited:May 2015

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