Publication
Title
Iteratively refining SVMs using priors
Author
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.
Language
English
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
Publication
New york : Ieee, 2015
ISBN
978-1-4799-9925-5
Volume/pages
(2015), p. 46-52
ISI
000380404600009
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.09.2016
Last edited 08.11.2017
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