Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary ...

This work is a natural extension of the work done in Part II of this series. A new partition of unity | the synchronized reproducing kernel (SRK) interpolant|is proposed within the framework of moving least square reproducing kernel representation. It is a further development and generalization of the reproducing kernel particle method (RKPM), which demonstrates some superior computational capa...

This paper presents parallelization strategies for the Radial Basis Function-Finite Difference (RBFFD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation but scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the f...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

In this paper identification of laryngeal disorders using cepstral parameters of human voice is researched. Mel-frequency cepstral coefficients (MFCCs), extracted from audio recordings of patient’s voice, are further approximated, using various strategies (sampling, averaging, and clustering by Gaussian mixture model). The effectiveness of similarity-based classification techniques in categoriz...

The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...

In our previous work we have shown that Mahalanobis kernels are useful for support vector classifiers both from generalization ability and model selection speed. In this paper we propose using Mahalanobis kernels for function approximation. We determine the covariance matrix for the Mahalanobis kernel using all the training data. Model selection is done by line search. Namely, first the margin ...

This paper investigates the ability of several models of Support Vector Machines (SVMs) with alternate kernel functions to predict the probability of occurrence of Essential Hypertension (HT) in a mixed patient population. To do this a SVM was trained with 13 inputs (symptoms) from the medical dataset. Different kernel functions, such as Linear, Quadratic, Polyorder (order three), Multi Layer P...

We explore ways of applying a prior on the covariance matrix of a Gaussian Process (GP) in order to increase its expressive power. We show that two well-known covariance priors, Wishart Process and Inverse Wishart Process, boil down to a two-layer feed-forward network of GPs with a particular kernel function on the neuron at the output layer. Both of these models perform supervised manifold lea...

A new type of Wavelet Neural Network (WNN) has been proposed to enhance the function approximation and generalization performance generating an optimal size network. In the proposed WNN, the nonlinear activation function is a linear combination of wavelets that can be updated during the network training process. As a result the approximate error is significantly decreased. The RBF network based...