Learning Algorithms Using Chance-Constrained Programs

Learning Algorithms Using Chance-Constrained Programs

This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with specified error rates, large dataset classification and Ordinal Regression (OR)....

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Bibliographic Details
Main Author: Jagarlapudi, Saketha Nath
Other Authors: Bhattacharyya, Chiranjib
Language:en_US
Published: 2010
Subjects:
Machine Learning
Classification
Dataset Classification
Ordinal Regression (OR)
Chance-Constrained Programming (CCP)
Classification - Algorithms
Ordinal Regression - Algorithms
Machine Learning - Algorithms
Second Order Cone Programs (SOCPs)
Maximum Margin Classification
Focused Crawling
Large Datasets
Error Rates
Computer Science
Online Access:http://hdl.handle.net/2005/733

Internet

http://hdl.handle.net/2005/733

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