In kernel-based learning algorithms the kernel function determines the scalar product and …
which the learning algorithm operates. The kernel is usually not adapted by the kernel method …

… embed evolutionary computation into statistical learning theory. First, we outline the connection
between large margin optimization and statistical learning … We then embed evolutionary …

… , the Evolutionary Kernel Machine (EKM), for the automatic design of data-specific kernels. …
Machine proposed in this paper aims to improve kernelbased nearest neighbor classification […

… a new combined kernel function and its learning method for … learning rate and better
classification performance. A set of simple kernel functions are com4 bined to create a new kernel …

… availability of kernel learning through investigating the adoption of MKL in the broad industry
applications. Kernel learning has been an active research topic in machine learning since …

… kernels [?]. More recently approaches for combining standard kernels with weights tuned by
an evolutionary … for using GEP for selection of evolutionary kernels. Some preliminary case …

… Our purpose is to find by using evolutionary techniques, new expression for the kernel
functions which are suitable for solving particular classification problems with SVM model. …

… In this paper we embed evolutionary computation into statistical learning theory. First, we …
usage of kernel functions just as for traditional SVM. We will show that evolutionary SVM are at …

… kernel into a non-PSD kernel, depending on the chosen GP function set. As non-PSD kernels
… to maintain the PSD property in the evolutionary process. This is achieved by incorporating …

… an improved learning algorithm named self-adaptive evolutionary extreme learning machine
(… (RBF) is used as the kernel function, the cost parameter C and the kernel parameter γ are …