Images from functional imaging experiments are subject to a data processing stream involving motion correction and spatial normalization. The next step is then to smooth the images using a Gaussian kernel. One reason for the smoothing step is to render activation information amenable to classical statistical inference via Random Field Theory. This requires that the residual fields have a smooth...

One of the central issues in kernel methods [5] is the problem of kernel selection (learning). This problem has recently received considerable attention which can range from the width parameter selection of Gaussian kernels to obtaining an optimal linear combination from a set of finite candidate kernels, see [3, 4]. In the latter case, kernel learning problem is often termed multi-kernel learn...

The optimization of kernel parameters is an important step in the application of the Relevance Vector Machine (RVM) for many real-world problems. In this paper, firstly we have developed an improved anisotropic Gaussian kernel as the kernel function of the RVM model, whose parameters are optimized by Bacterial Foraging Optimization (BFO). Then the proposed method is applied to describing the pr...

Inspired by studies of cognitive psychology, we proposed a new dynamic similarity kernel for visual recognition. This kernel has great consistency with human visual similarity judgement by incorporating the perceptual distance function. Moreover, this kernel can be seen as an extension of Gaussian kernel, and therefore can deal with nonlinear variations well like the traditional kernels. Experi...

Complex-valued signals are used in the modeling of many systems in engineering and science, hence being of fundamental interest. Often, random complex-valued signals are considered to be proper. A proper complex random variable or process is uncorrelated with its complex conjugate. This assumption is a good model of the underlying physics in many problems, and simplifies the computations. While...

Two years ago, Ryan Weber developed a simple support vector machine for protein secondary structure prediction to investigate the complexity of kernel and algorithm necessary to perform adequate classification [1]. For my project, I have resurrected Ryan‘s project, tested his conclusions, and extended the support vector machine to use the spectrum string kernel designed by Leslie, Eskin, and No...

Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that storing and computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood, an approximation that accelerates such computation significantly. Key to Fastfood is the observation that Hadamard mat...

One major SVM weakness has been the use of generic kernel functions to compute distances among data points. Polynomial, linear, and Gaussian are typical examples. They do not take full advantage of the inherent probability distributions of the data. Focusing on audio speaker identification and verification, we propose to explore the use of novel kernel functions that take full advantage of good...

Spectral clustering has been successfully used in the field of pattern recognition and image processing. The efficiency of spectral clustering, however, depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the Gaussian kernel function is parameter sensitive and the Euclidean distance ...