Large Scale Multiple Kernel Learning
Large Scale Multiple Kernel Learning
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发表时间：
2006-12
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通讯作者：
S. Sonnenburg;Gunnar Rätsch;C. Schäfer;B. Scholkopf
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作者：
S. Sonnenburg;Gunnar Rätsch;C. Schäfer;B. Scholkopf
虽然经典的基于核的学习算法是基于单个核的，但在实践中，通常希望使用多个核。Lanckriet et al.(2004)考虑了核矩阵的圆锥组合用于分类，导致了一个凸二次约束二次规划。我们证明了它可以被重写为一个半无限线性规划，可以通过循环使用标准的支持向量机实现来有效地求解。此外，我们将该公式和我们的方法推广到更大的一类问题，包括回归和单类分类。实验结果表明，该算法适用于数十万个样本或数百个核的组合，有助于模型的自动选择，提高了学习结果的可解释性。在第二部分中，我们讨论了支持向量机的一般加速机制，特别是当使用稀疏特征映射时，就像对字符串核一样，允许我们在来自计算生物学的1000万个真实世界拼接数据集上训练字符串核支持向量机。我们在我们的机器学习工具箱幕府中集成了多核学习，其源代码可在http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun上公开获得。
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for SVMs, especially when used with sparse feature maps as appear for string kernels, allowing us to train a string kernel SVM on a 10 million real-world splice data set from computational biology. We integrated multiple kernel learning in our machine learning toolbox SHOGUN for which the source code is publicly available at http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun .