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Memory-efficient large-scale linear support vector machine

Alrajeh, Abdullah, Takeda, Akiko and Niranjan, Mahesan (2014) Memory-efficient large-scale linear support vector machine At The 7th International Conference on Machine Vision, Italy. 19 - 21 Nov 2014.

Record type: Conference or Workshop Item (Paper)


Stochastic gradient descent has been advanced as a computationally efficient method for large-scale problems. In classification problems, many proposed linear support vector machines as very effective classifiers. However, they assume that the data is already in memory which might not be always the case. Recent work suggests a classical method that divides such a problem into smaller blocks and then solves the sub-problems iteratively. We show that a simple modification of shrinking the dataset early will produce significant saving in computation and memory. We further ?nd that on problems larger than previously considered, our approach is able to reach solutions on top-end desktop machines while competing methods cannot.

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Published date: November 2014
Venue - Dates: The 7th International Conference on Machine Vision, Italy, 2014-11-19 - 2014-11-21
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Organisations: Electronics & Computer Science


Local EPrints ID: 368208
PURE UUID: 0bd228fe-5db4-43fe-a99e-534039190ef2

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Date deposited: 20 Aug 2014 15:26
Last modified: 18 Jul 2017 01:50

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Author: Abdullah Alrajeh
Author: Akiko Takeda

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