Fast Linear SVM Validation Based on Early Stopping in Iterative Learning
Fast Linear SVM Validation Based on Early Stopping in Iterative Learning
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DOI：
10.1142/s0218001415510131
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发表时间：
2015-12-01
影响因子：
1.5
通讯作者：
Azimifar, Zohreh
中科院分区：
文献类型：
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作者：
Famouri, Mahmoud;Taheri, Mohammad;Azimifar, Zohreh
分类是机器学习和模式识别中的一个重要领域。在最近邻分类器、神经网络分类器和贝叶斯分类器等分类器中，支持向量机（SVM）被认为是一种非常强大的分类器。与其他方法相比，支持向量机的优点之一是其高效和可调的泛化能力。支持向量机分类器的性能取决于其参数，特别是正则化参数C，通常通过交叉验证来选择。支持向量机虽然具有泛化性，但也存在一定的局限性，比如它的低速训练阶段比较长。交叉验证是训练阶段非常耗时的一部分，因为对于参数C的任何候选值，必须完全重复整个训练和验证过程。本文提出了一种新的支持向量机学习算法的早期停止方法。通过将验证部分整合到SVM训练的优化部分中，而不会损失分类器的通用性或降低分类器的性能，从而实现提前停止。此外，该方法可以与其他可用的加速器方法结合使用，因为我们提出的方法与其他加速器方法之间没有任何依赖关系，因此不会发生冗余。我们的方法在各种IJCI存储库数据集上进行了测试和验证，结果表明该方法在不失去任何通用性或影响分类器最终模型的情况下加快了SVM的学习阶段。
Classification is an important field in machine learning and pattern recognition. Amongst various types of classifiers such as nearest neighbor, neural network and Bayesian classifiers, support vector machine (SVM) is known as a very powerful classifier.One of the advantages of SVM in comparison with the other methods, is its efficient and adjustable generalization capability. The performance of SVM classifier depends on its parameters, specially regularization parameter C, that is usually selected by cross-validation. Despite its generalization, SVM suffers from some limitations such as its considerable low speed training phase. Cross-validation is a very time consuming part of training phase, because for any candidate value of the parameter C, the entire process of training and validating must be repeated completely.In this paper, we propose a novel approach for early stopping of the SVM learning algorithm. The proposed early stopping occurs by integrating the validation part into the optimization part of the SVM training without losing any generality or degrading performance of the classifier. Moreover, this method can be considered in conjunction with the other available accelerator methods since there is not any dependency between our proposed method and the other accelerator ones, thus no redundancy will happen. Our method was tested and verified on various IJCI repository datasets and the results indicate that this method speeds up the learning phase of SVM without losing any generality or affecting the final model of classifier.