In this paper, we apply the Laplace decomposition method to obtain a series solutions of the Burgers-Huxley and Burgers-Fisher equations. The technique is based on the application of Laplace transform to nonlinear partial differential equations. The method does not need linearization, weak nonlinearity assumptions or perturbation theory and the nonlinear terms can be easily handled by using the...

This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmen...

A modified Fisher discriminate analysis method for classifying stream data is presented. To satisfy the realtime demand in classifying stream data, this method defines a new criterion for Fisher discriminate analysis. Since the new criterion requires less computation and memory space, it is much faster and more suitable for online processing in stream data environment. It can overcome the probl...

Fisher score and genetic algorithm are widely used for feature selection. However, some redundant features will be selected by Fisher score, and the convergence properties may be worse if the initial population of genetic algorithm is generated by a random manner. To improve the performance of feature selection by Fisher score and genetic algorithm, we propose a hybrid feature selection method,...

Local Fisher Discriminant Analysis (LFDA) is a feature extraction method which combines the ideas of Fisher discriminant analysis (FDA) and locality preserving projection (LPP). It works well for multimodal problems. But LFDA suffers from the under-sampled problem of the linear discriminant analysis (LDA). To deal with this problem, we propose a regularized orthogonal local Fisher discriminant ...

We develop a method for recognizing the emotional state of a person who is deliberately expressing one of eight emotions. Four physiological signals were measured and six features of each of these signals were extracted. We investigated three methods for the recognition: (1) Sequential oating forward search (SFFS) feature selection with K-nearest neighbors classiication, (2) Fisher projection o...

Kernel Fisher discriminant analysis (KFD) is an effective method to extract nonlinear discriminant features of input data using the kernel trick. However, conventional KFD algorithms endure the kernel selection problem as well as the singular problem. In order to overcome these limitations, a novel nonlinear feature extraction method called adaptive quasiconformal kernel Fisher discriminant ana...