In recent years some attempts have been done to relate the RBF with wavelets [1,2] in handling high dimensional multiscale problems. To the author’s knowledge, however, the orthonormal and bi-orthogonal RBF wavelets are still missing in the literature. By using the nonsingular general solution and singular fundamental solution of differential operator [3], recently the present author made some ...

This paper aims to survey our recent work relating to the radial basis function (RBF) from some new views of points. In the first part, we established the RBF on numerical integration analysis based on an intrinsic relationship between the Green's boundary integral representation and RBF. It is found that the kernel function of integral equation is important to create efficient RBF. The fundame...

In this paper, an evaluation using various training data sets for discrimination of dysmorphic facial features with distinctive information will be presented. We utilize Gabor Wavelet Transform (GWT) as feature extractor, K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) as statistical classifiers. We analyzed the classification accuracy according to increasing dimension of training d...

Recent works in speech recognition technology, classification techniques is focused on models, such as support vector machines (SVMs), in order to improve the generalization ability of the machine learning for noisy environments. However kernel function plays a vital role in the generalization ability of the SVMs. This paper address, the issue of noise robustness for an Automatic Speech Recogni...

In this paper, we investigate an approach for classification of mammographic masses as benign or malign. This study relies on a combination of Support Vector Machine (SVM) and wavelet-based subband image decomposition. Decision making was performed in two stages as feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features....

According to the fact that parameter selection of support vector machine(SVM) for fault diagnosis is difficult, a new method based on bacterial foraging algorithm(BAF) for support vector machine parameter optimization was proposed , then the faster optimization of the parameters C and RBF kernel parameter γ was performed. The crack rotor as the experiment object, firstly, AE signal of rotors wi...

In this paper the accuracy of two machine learning algorithms including SVM and Bayesian Network are investigated as two important algorithms in diagnosis of Parkinson’s disease. We use Parkinson's disease data in the University of California, Irvine (UCI). In order to optimize the SVM algorithm, different kernel functions and C parameters have been used and our results show that SVM with C par...

Content Based Image Retrieval is a technique which uses visual contents for searching images from large scale image databases Information extracted from images such as color, texture and shape are known as feature vectors. Using multiple feature vectors to describe an image during retrieval process increases the accuracy when compared to the retrieval using single feature vector. The objective ...

Feature Extraction is the process to extract image features to a distinguishable extent. Information extracted from images such as color, texture and shape are known as feature vectors. Using multiple feature vectors to describe an image during retrieval process increases the accuracy when compared to the retrieval using single feature vector. The objective of this paper is to analyze the perfo...