Title
Iteratively refining SVMs using priors Iteratively refining SVMs using priors
Author
Faculty/Department
Faculty of Applied Economics
Publication type
conferenceObject
Publication
New york :Ieee ,
Subject
Computer. Automation
Source (journal)
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA
Source (book)
IEEE International Conference on Big Data, OCT 29-NOV 01, 2015, Santa Clara, CA
Volume/pages
(2015) , p. 46-52
ISBN
978-1-4799-9925-5
ISI
000380404600009
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Research on scalable machine learning algorithms has gained a considerable amount of traction since the exponential growth in data assets during the past decades. Many Big Data applications resort to somewhat "simple" data modelling techniques due to the computational constraints associated with more complex models. Simple models, while being very efficient to estimate, often fail to capture some of the finer details of more complex datasets. In this manuscript, we explore the idea that complex large scale classification can be tractable using a process of iterative refining. In such a process, we focus on non-linearities of the data only after having first found an approximate linear model. This knowledge is then incorporated into the non-linear model implicitly, allowing the non-linear model to focus on important parts of the data after a rough first estimation. This in turn reduces overall training time and allows for a richer model representation, eventually leading to more predictive power.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000380404600009&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000380404600009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
https://repository.uantwerpen.be/docman/iruaauth/3414a4/135004.pdf
Handle