We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is h...

Although there is a large diversity in the literature related to kernel methods, there are only a few works which do not use kernels based on Radial Basis Functions (RBF) for regression problems. The reason for that is that they present very good generalization capabilities and smooth interpolation. This paper studies an initial framework to create specific-to-problem kernels for application to...

Regularized empirical risk minimization using kernels and their corresponding reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine learning. However, the actually used kernel often depends on one or on a few hyperparameters or the kernel is even data dependent in a much more complicated manner. Examples are Gaussian RBF kernels, kernel learning, and hierarchical Gaussian...

This paper aims at bridging a gap between functional ANOVA modeling and recent advances in machine learning and kernel-based models. Functional ANOVA on the one hand extends linear ANOVA techniques to nonlinear multivariate models as smoothing splines, aiming to provide interpretability to an estimate and handling the curse of dimensionality. Multiple kernel learning (MKL) on the other hand is ...

We determine the asymptotic limit of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is held ...

Authors of short papers such as letters or editorials often express complementary opinions, and sometimes contradictory ones, on related work in previously published articles. The MEDLINE® citations for such short papers are required to list bibliographic data on these “commented on” articles in a “CON” field. The challenge is to automatically identify the CON articles referred to by the author...

On certain types of multi-touch touchpads, determining the number of finger stroke is a non-trivial problem. We investigate the application of several classification algorithms to this problem. Our experiments are based on a flat prototype of the spherical Touchglobe touchpad. We demonstrate that with a very short delay after the stroke, the number of touches can be determined by a Support Vect...

In this manuscript handwritten Gurmukhi character recognition for isolated characters is proposed. We have used Gabor Filter based method for feature extraction. Our database consists of 200 samples of each of basic 35 characters of Gurmukhi s cript collected from different writers. These samples are pre-processed and normalized to 32*32 sizes. The highest accuracy obtained is 94.29% as 5-fold ...

Prediction of barley yield is an attempt to accurately forecast the outcome of a specific situation, using as input information extracted from a set of data features that potentially describe the situation. In this study, an attempt has been made to analyze and compare multiple linear regression (MLR), and artificial neural network (ANN) including multi-layer p erceptron (MLP) and r adial basis...

Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introduced, which has been shown to outperform previous semi-supervised clustering approaches. However, the setting of the kernel’s parameter is left to manual tuning, and the chosen value can largely affect the quality of ...