Linear support vector machines via dual cached loops
Linear support vector machines via dual cached loops
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DOI：
10.1145/2339530.2339559
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发表时间：
2012-08
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影响因子：
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通讯作者：
Shin Matsushima;S. Vishwanathan;Alex Smola
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文献类型：
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作者：
Shin Matsushima;S. Vishwanathan;Alex Smola
现代计算机硬件提供了具有不同速度、容量和相关成本的存储子系统的精细层次结构。此外，处理器现在本质上是并行的，可以同时执行多个不同的线程。本文提出了流式支持向量机，这是第一个训练线性支持向量机（svm）的算法，它通过将缓存与优化相结合来利用这些特性。StreamSVM的工作原理是在双对象中执行更新，从而避免了重新平衡频繁访问的示例的需要。此外，我们通过按需生成特性来动态地权衡文件I/O和数据扩展。这大大提高了吞吐量。实验表明，StreamSVM优于其他线性SVM求解器，包括获奖的[38]，在数量级上，并在更短的时间内产生更准确的解。
Modern computer hardware offers an elaborate hierarchy of storage subsystems with different speeds, capacities, and costs associated with them. Furthermore, processors are now inherently parallel offering the execution of several diverse threads simultaneously. This paper proposes StreamSVM, the first algorithm for training linear Support Vector Machines (SVMs) which takes advantage of these properties by integrating caching with optimization. StreamSVM works by performing updates in the dual, thus obviating the need to rebalance frequently visited examples. Furthermore we trade off file I/O with data expansion on the fly by generating features on demand. This significantly increases throughput. Experiments show that StreamSVM outperforms other linear SVM solvers, including the award winning work of [38], by orders of magnitude and produces more accurate solutions within a shorter amount of time.