We consider kernel based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. We find that the risk minimizer remains unchanged if we constrain the risk minimization to hypothesis spaces induced by suitable kernel functions. We show that not all kernel ...

Received Oct 19, 2017 Revised Dec 22, 2017 Accepted Jan 14, 2018 Dyslexia is referred as learning disability that causes learner having difficulties in decoding, reading and writing words. This disability associates with learning processing region in the human brain. Activities in this region can be examined using electroencephalogram (EEG) which record electrical activity during learning proce...

In a recent work, we have proposed the quantized kernel least mean square (QKLMS) algorithm, which is quite effective in online learning sequentially a nonlinear mapping with a slowly growing radial basis function (RBF) structure. In this paper, in order to further reduce the network size, we propose a sparse QKLMS algorithm, which is derived by adding a sparsity inducing 1 l norm penalty of th...

We show that support vector machines of the 1-norm soft margin type are universally consistent provided that the regularization parameter is chosen in a distinct manner and the kernel belongs to a specific class}the so-called universal kernels}which has recently been considered by the author. In particular it is shown that the 1-norm soft margin classifier with Gaussian RBF kernel on a compact ...

We propose a new method for constructing hyperkenels and define two promising special cases that can be computed in closed form. These we call the Gaussian and Wishart hyperkernels. The former is especially attractive in that it has an interpretable regularization scheme reminiscent of that of the Gaussian RBF kernel. We discuss how kernel learning can be used not just for improving the perform...

Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination of a static and a sequential SVM classifier. A Gaussian dynamic time warping based kernel is used in the sequential classifier. The system is vali...

We propose the “generalized min-max” (GMM) kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named “generalized consistent weighted sampling” (GCWS) which linearizes this (nonlinear) kernel. A natural competitor of GMM is the radial basis function (RBF) kernel, whose correspond...

Statistical downscaling is an effort to link global scale to local scale variable. It uses GCM model which usually used as a prime instrument in learning system of various climate. The purpose of this study is as a SD model by using SVR in order to predict the rainfall in dry season; a case study at Indramayu. Through the model of SD, SVR is created with linear kernel and RBF kernel. The result...

An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed the learned radial basis function (RBF) as research object. here can be considered a subspace region consisting of same pattern category in training sample space. By extending space original instances, relevant information between instances obtained from subspace, c...

We use methods from the Fock space and Segal–Bargmann theories to prove several results on Gaussian RBF kernel in complex analysis. The latter is one of most used kernels modern machine learning support vector classification algorithms. Complex analysis techniques allow us consider notions linked radial basis function (RBF) kernels, such as feature map, using so-called transform. also show how ...