On Kernel-Target Alignment
On Kernel-Target Alignment
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DOI：
10.1007/3-540-33486-6_8
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发表时间：
2001-01
期刊：
影响因子：
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通讯作者：
N. Cristianini;J. Shawe-Taylor;A. Elisseeff;J. Kandola
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文献类型：
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作者：
N. Cristianini;J. Shawe-Taylor;A. Elisseeff;J. Kandola
我们引入了核对齐的概念，这是两个核函数之间或一个核函数与一个目标函数之间的相似性的度量。这个量捕获内核和给定学习任务之间的一致程度，并且在机器学习中具有非常自然的解释，也导致用于模型选择和学习的简单算法。我们分析了它的理论性质，证明了它是尖锐地集中在其期望值，我们讨论了它与其他标准措施的每（cid：173）。最后，我们描述了一些可以在这个框架内获得的算法，给出的实验结果表明，调整内核以改善标记数据的对齐显着增加了测试集上的对齐，从而提高了分类精度。因此，该方法提供了进行转导的原理性方法。
We introduce the notion of kernel-alignment, a measure of similar (cid: 173) ity between two kernel functions or between a kernel and a target function. This quantity captures the degree of agreement between a kernel and a given learning task, and has very natural interpre (cid: 173) tations in machine learning, leading also to simple algorithms for model selection and learning. We analyse its theoretical properties, proving that it is sharply concentrated around its expected value, and we discuss its relation with other standard measures of per (cid: 173) formance. Finally we describe some of the algorithms that can be obtained within this framework, giving experimental results show (cid: 173) ing that adapting the kernel to improve alignment on the labelled data significantly increases the alignment on the test set, giving improved classification accuracy. Hence, the approach provides a principled method of performing transduction.