A Fast Parallel Optimization for Training Support Vector Machine
A Fast Parallel Optimization for Training Support Vector Machine
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DOI：
10.1007/3-540-45065-3_9
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发表时间：
2003-07
期刊：
影响因子：
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通讯作者：
Jian-xiong Dong;A. Krzyżak;C. Suen
中科院分区：
文献类型：
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作者：
Jian-xiong Dong;A. Krzyżak;C. Suen
提出了一种并行优化和顺序优化相结合的多类SVM快速训练算法。并行优化步骤的主要优点是快速去除大多数非支持向量，这大大减少了顺序优化阶段的训练时间。此外，在算法中有效地引入了内核缓存、收缩和调用BLAS函数等策略，加快了训练速度。在MNIST手写体数字库上的实验表明，与Keerthi等人提出的算法相比，该算法在不牺牲泛化性能的前提下，速度提高了110倍。s修改的SMO。此外，我们首次在手写体中文数据库ETL9B上研究了SVM的训练性能，该数据库包含3000多个类别和大约50万个训练样本。总培训时间仅为5.1小时。ETL9B的原始错误率为1.1%。
A fast SVM training algorithm for multi-classes consisting of parallel and sequential optimizations is presented. The main advantage of the parallel optimization step is to remove most non-support vectors quickly, which dramatically reduces the training time at the stage of sequential optimization. In addition, some strategies such as kernel caching, shrinking and calling BLAS functions are effectively integrated into the algorithm to speed up the training. Experiments on MNIST handwritten digit database have shown that, without sacrificing the generalization performance, the proposed algorithm has achieved a speed-up factor of 110, when compared with Keerthi et al.’s modified SMO. Moreover, for the first time ever we investigated the training performance of SVM on handwritten Chinese database ETL9B with more than 3000 categories and about 500,000 training samples. The total training time is just 5.1 hours. The raw error rate of 1.1% on ETL9B has been achieved.