Work

Data subset selection for efficient SVM training

Year: 2017

Type: conference-paper

Abstract: Training a support vector machine (SVM) on large data sets is a computationally intensive task. In this paper, we study the problem of selecting a subset of data for training the SVM classifier under requirement that the loss of performance due to training data reduction is low. A function quantifying suitability of a selected subset is proposed, and a greedy algorithm for solving the subset selection problem is introduced. The algorithm is evaluated on hand digit recognition and other binary classification tasks, and its performance is compared to stratified sampling methods.

Source: European Signal Processing Conference (EUSIPCO)

DOI: 10.23919/eusipco.2017.8081324

Authors: Sara Mourad, Ahmed Tewfik, Haris Vikalo

Institution: The University of Texas at Austin

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2765940401

FWCI: 0.44

Cites: 31

Cited by: 5

Related to: 10