NDLI: Adaptive Budget for Online Learning

Content Provider	IEEE Xplore Digital Library
Author	Tabatabaei, T.S. Karray, F. Kamel, M.S.
Copyright Year	2013
Description	Author affiliation: Centre for Pattern Anal. & Machine Intell., Univ. of Waterloo, Waterloo, ON, Canada (Tabatabaei, T.S.; Karray, F.; Kamel, M.S.)
Abstract	Although the perceptron algorithm has been considered a simple supervised learning algorithm, it has the advantage of learning from the training data set one at a time. This makes it more suitable for online learning tasks and new families of kernelized perceptrons have been shown to be effective in handling streaming data. However, the amount of memory required for storing the online model which grows without any limits and the consequent excessive computation and time complexity makes this framework infeasible in real problems. A common solution to this restriction is to limit the allowed budget size and discard some of the examples in the memory when the budget size is exceeded. In this paper we present a framework for choosing a proper adaptive budget size based on underlying properties of data streams. The experimental results on several synthetic and real data sets show the efficiency of our proposed system compared to other algorithms.
Sponsorship	Toshiba
Starting Page	577
Ending Page	583
File Size	289215
Page Count	7
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis Computational modeling concept drift incremental learning Prediction algorithms Data models Internet Kernel Online learning
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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