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Title:Reducing Training Time with More Data - a Review
Author(s):Brando Miranda, Michael Wu and He Sun
Subject(s):machine learning
statistical machine learning
machine learning theory
ai
artificial intelligence
SVM
pegasos
algorithmic aspects of machine learning
Abstract:In most machine learning problems, we tend to think that training algorithms require more computation time as the number of training samples increases. In this paper we discuss two contexts in which this is not true. In the case of SVM optimization, assuming some desired generalization error, the PEGASOS algorithm statistically needs less runtime with more data. In the case of learning halfspaces over sparse vectors, more training examples reduce the training runtime from exponential to polynomial time.
Issue Date:2015-05-14
Genre:Technical Report
Article
Type:Text
URI:http://hdl.handle.net/2142/112789
Date Available in IDEALS:2021-11-24


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