Minimizing the cross validation error to mix kernel matrices of heterogeneous biological data
Minimizing the cross validation error to mix kernel matrices of heterogeneous biological data
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DOI：
10.1023/b:nepl.0000016845.36307.d7
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发表时间：
2004-02-01
影响因子：
3.1
通讯作者：
Asai, K
中科院分区：
文献类型：
--
作者：
Tsuda, K;Uda, S;Asai, K
在生物数据中，通常情况下，对象以两种或更多种表示来描述。为了根据这些数据进行分类，我们必须以某种方式将它们联合收割机组合起来。在内核机器的上下文中，这个任务相当于将几个内核矩阵混合成一个。在本文中，我们提出了两种混合核矩阵的方法，其中混合权重被优化以最小化交叉验证误差。在细菌分类和基因功能预测实验中，我们的方法在大多数情况下显着优于单核分类器。
In biological data, it is often the case that objects are described in two or more representations. In order to perform classification based on such data, we have to combine them in a certain way. In the context of kernel machines, this task amounts to mix several kernel matrices into one. In this paper, we present two ways to mix kernel matrices, where the mixing weights are optimized to minimize the cross validation error. In bacteria classification and gene function prediction experiments, our methods significantly outperformed single kernel classifiers in most cases.