In this paper, we present a new kernel for unordered sets of data of the same type. It works by first fitting a set with a Gaussian mixture, then evaluate an efficient kernel on the two fitted Gaussian mixtures. Furthermore, we show that this kernel can be extended to sets embedded in a feature space implicitly defined by another kernel, where Gaussian mixtures are fitted with the kernelized EM...

Gaussian kernel regularization is widely used in the machine learning literature and has proved successful in many empirical experiments. The periodic version of Gaussian kernel regularization has been shown to be minimax rate optimal in estimating functions in any finite order Sobolev space. However, for a data set with n points, the computation complexity of the Gaussian kernel regularization...

since mammography images are in low-contrast, applying enhancement techniques as a pre-processing step are wisely recommended in classification of the abnormal lesions into benign or malignant. a new kind of structural enhancement is proposed by morphological operator which introduces an optimal gaussian kernel primitive, the kernel parameters are optimized the use of genetic algorithm­­. we al...

Kernel methods and rough sets are two general pursuits in the domain of machine learning and intelligent systems. Kernel methods map data into a higher dimensional feature space, where the resulting structure of the classification task is linearly separable; while rough sets granulate the universe with the use of relations and employ the induced knowledge granules to approximate arbitrary conce...

We consider binary classification using Support Vector Machines with Gaussian kernels: KΣ(xi, xj) = e −(xi−xj)′Σ−1(xi−xj) and address the problem of selecting a covariance matrix Σ which gives good classification performance. As with other nonparametric classification methods based on distances between data points (such as nearest neighbor and Parzen methods), the choice of distance function (Σ...