Parallel decomposition approaches for training support vector machines

Serafini, Thomas; Zanghirati, Gaetano; Zanni, Luca

doi:10.1016/s0927-5452(04)80035-2

Cited by 4 publications

(1 citation statement)

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“…-A distributed SVM algorithm for row-wise and column-wise data distribution is described in [26], which so far can be used for linear SVMs only. -A promising parallel MPI-based decomposition solver for training support vector machines has been implemented recently [30]. -A parallel support vector machine for multi-processor shared memory (SMP) clusters has been introduced in [31].…”

Section: Parallel Support Vector Machine Approachesmentioning

confidence: 99%

HyParSVM – A New Hybrid Parallel Software for Support Vector Machine Learning on SMP Clusters

Eitrich¹,

Frings²,

Lang

2006

Euro-Par 2006 Parallel Processing

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Abstract. In this paper we describe a new hybrid distributed/shared memory parallel software for support vector machine learning on large data sets. The support vector machine (SVM) method is a well-known and reliable machine learning technique for classification and regression tasks. Based on a recently developed shared memory decomposition algorithm for support vector machine classifier design we increased the level of parallelism by implementing a cross validation based on message passing. With this extention we obtained a flexible parallel SVM algorithm that can be used on high-end machines with SMP architectures to process the large data sets that arise more and more in bioinformatics and other fields of research.

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Section: Parallel Support Vector Machine Approachesmentioning

confidence: 99%

HyParSVM – A New Hybrid Parallel Software for Support Vector Machine Learning on SMP Clusters

Eitrich¹,

Frings²,

Lang

2006

Euro-Par 2006 Parallel Processing

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SVM Based Prediction of Bacterial Transcription Start Sites

Gordon

Towsey

2005

Lecture Notes in Computer Science

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Abstract. Identifying bacterial promoters is the key to understanding gene expression. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). Knowing the TSS position, one can predict promoter positions to within a few base pairs, and vice versa. As a route to promoter identification, we formally address the problem of TSS prediction, drawing on the RegulonDB database of known (mapped) Escherichia coli TSS locations.The accepted method of finding promoters (and therefore TSSs) is to use position weight matrices (PWMs). We use an alternative approach based on support vector machines (SVMs). In particular, we quantify performance of several SVM models versus a PWM approach, using area under the detection-error tradeoff (DET) curve as a performance metric. SVM models are shown to outperform the PWM at TSS prediction, and to substantially reduce numbers of false positives, which are the bane of this problem.

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