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Trading Representability for Scalability: Adaptive Multi-Hyperplane Machine for Nonlinear Classification ABSTRACT
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@MISC{Wang_tradingrepresentability,
author = {Zhuang Wang and Koby Crammer},
title = {Trading Representability for Scalability: Adaptive Multi-Hyperplane Machine for Nonlinear Classification ABSTRACT},
year = {}
}
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Abstract
Support Vector Machines (SVMs) are among the most popular and successful classification algorithms. Kernel SVMs often reach state-of-the-art accuracies, but suffer from the curse of kernelization due to linear model growth with data size on noisy data. Linear SVMs have the ability to efficiently learn from truly large data, but they are applicable to a limited number of domains due to low representational power. To fill the representability and scalability gap between linear and nonlinear SVMs, we propose the Adaptive Multi-hyperplane Machine (AMM) algorithm that accomplishes fast training and prediction and has capability to solve nonlinear classification problems. AMM model consists of a set of hyperplanes (weights), each assigned to one of the multiple classes, and predicts based on the associated class of the weight that provides the largest prediction. The
Keyphrases
adaptive multi-hyperplane machine nonlinear classification abstract trading representability scalability gap nonlinear svms linear model growth associated class nonlinear classification problem linear svms amm model kernel svms support vector machine noisy data large data multiple class state-of-the-art accuracy low representational power data size successful classification algorithm limited number
