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...

This chapter introduces a novel Levenberg-Marquardt like second-order algorithm for tuning the Parzen window σ in a Radial Basis Function (Gaussian) kernel. In this case, each attribute has its own sigma parameter associated with it. The values of the optimized σ are then used as a gauge for variable selection. In this study, the Kernel Partial Least Squares (K-PLS) model is applied to several ...

In this exploratory paper we study the convergence rates of an iterated method for approximating derivatives of periodic functions using radial basis function (RBF) interpolation. Given a target function sampled on some node set, an approximation of the m derivative is obtained by m successive applications of the operator “interpolate, then differentiate” this process is known in the spline com...

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...

A self tuning PID control strategy using reinforcement learning is proposed in this paper to deal with the control of wind energy conversion systems (WECS). Actor-Critic learning is used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to impro...

We describe a system for the CoNLL2005 shared task of Semantic Role Labeling. The system implements a two-layer architecture to first identify the arguments and then to label them for each predicate. The components are implemented as SVM classifiers using libSVM. Features were adapted and tuned for the system, including a reduced set for the identifier classifier. Experiments were conducted to ...

A B-spline kernel combined with RBF is developed, a mixed kernel is obtained. By analyzing the structure of the logging signal characteristics, the method is used to automatically identify the water-flooded status of oilsaturated stratum. The experimental results show that the mixed kernel has high recognition accuracy with the advantages of the short running time.

A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving properties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering and denoising algorithms such as bilateral ...

-In this paper, we introduce a new classification kernel by embedding self organized map (SOM) clustering with mixture of radial basis function (RBF) networks. The model’s efficacy is demonstrated in solving a multi-class TIMIT speech recognition problem where the kernel is used to learn the multidimensional cepstral feature vectors to estimate their posterior class probabilities. The tests res...

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...