PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

COFFIN: A Computational Framework for Linear SVMs
Sören Sonnenburg and Vojtech Franc
In: Proceedings of the 27nd International Machine Learning Conference, Haifa, Israel(2010).

Abstract

In a variety of applications, kernel machines such as Support Vector Machines (SVMs) have been used with great success often delivering state-of-the-art results. Using the kernel trick, they work on several domains and even enable heterogeneous data fusion by concatenating feature spaces or multiple kernel learning. Unfortunately, they are not suited for truly large-scale applications since they suffer from the curse of supporting vectors, e.g., the speed of applying SVMs decays linearly with the number of support vectors. In this paper we develop COFFIN --- a new training strategy for linear SVMs that effectively allows the use of on demand computed kernel feature spaces and virtual examples in the primal. With linear training and prediction effort this framework leverages SVM applications to truly large-scale problems: As an example, we train SVMs for human splice site recognition involving 50 million examples and sophisticated string kernels. Additionally, we learn an SVM based gender detector on 5 million examples on low-tech hardware and achieve beyond the state-of-the-art accuracies on both tasks.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7940
Deposited By:Sören Sonnenburg
Deposited On:17 March 2011