Selective algorithms for large-scale classification and structured learning

Chang, Kai-Wei

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https://hdl.handle.net/2142/78437 Copy

Description

Title

Selective algorithms for large-scale classification and structured learning

Author(s)

Chang, Kai-Wei

Issue Date

2015-04-23

Director of Research (if dissertation) or Advisor (if thesis)

Roth, Dan

Doctoral Committee Chair(s)

Roth, Dan

Committee Member(s)

• Forsyth, David A.
• Zhai, ChengXiang
• Platt, John

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(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.

Graduation Semester

2015-5

Type of Resource

text

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http://hdl.handle.net/2142/78437

Copyright and License Information

Copyright 2015 Kai-Wei Chang

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