the wireless channel, and using 1-Class SVM (Support Vector Machine) algorithm for the pre-processing, KFD (Kernel Fisher Discriminant) and k-NN (Nearest Neighbour) algorithm for the precise classification. And there were 32 volunteers, 12 elders (age 60-80) and 20 younger (age 20-39), attended our experiments

We investigate a new kernel-based classifier: the Kernel Fisher Discriminant (KFD). A mathematical programming formulation based on the observation that KFD maximizes the average margin permits an interesting modification of the original KFD algorithm yielding the sparse KFD. We find that both, KFD

CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.

principales. Les points communs exhibés permettent de proposer un algorithme itératif pour celle-ci également. Il présente de fait des similitudes marquées avec celui dédié à KFD. Abstract – In recent years, many detection methods based on the reproducing kernel Hilbert spaces have been developed

Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for constructing a kernel Fisher discriminant (KFD) from training examples with noisy labels. The approach allows

In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is

We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a kernel function, we incorporate the task of choosing an appropriate kernel into the optimization

individually associates with a scaling factor. A novel algorithm, named FS-KFD, is developed to tune the scaling factors and regularization parameters for the feature-scaling kernel. The proposed algorithm is based on optimizing the smooth leave-one-out error via a gradient-descent method and has been

In this paper we use kernel-based Fisher Discriminants (KFD) for classification by integrating this method in a HMM-based speech recognition system. We translate the outputs of the KFD-classifier into conditional probabilities and use them as production probabilities of a HMM-based decoder

consider the class of kernel based learning techniques. The main contributions of this work can be summarized as follows. Building upon the theory of reproducing kernels we propose a number of new learning algorithms based on the maximization of a Rayleigh coefficient in a kernel feature space. We