【摘要】 核方法是近年发展起来的一种新的机器学习方法,它可在高维(特征)空间中用线性的方法有效地解决低维(输入)空间中线性不可分问题.采用核方法,在Mika提出的核Fisher判别基础上,给出Fisher判别分析从输入空间变换到特征空间的数学过程(核化过程),并对特征空间中投影向量可由训练样本线性表示问题予以证明.更多还原

【Abstract】 kernel method has been of wide concern in the field of machine learning recently. It allows the efficient computation of linear classification in high-dimensional feature space, instead of non-linearly separable problem in low-dimensional input space. This paper, based on the Mika’s KFD, discusses how kernel methods work from input sapce to feature space in detail with mathematical derivation. Furthermore, the proposition that the project vector can be repersented by linear combination of training samples in feature space has been proved also.更多还原