Classification of biological sequences with kernel methods Kernels and kernel methods Kernels for biological sequences Summary Outline 1 Kernels and kernel methods Kernels Kernel Methods 2 Kernels for biological sequences Motivations Feature space approach Using generative models Derive from a similarity measure Application: remote homology detection Jean-Philippe Vert Classification of biologi...

In this paper we show how the generation of documents can be thought of as a k-stage Markov process, which leads to a Fisher kernel from which the n-gram and string kernels can be reconstructed. The Fisher kernel view gives a more exible insight into the string kernel and suggests how it can be parametrised in a way that re-ects the statistics of the training corpus. Furthermore, the prob-abili...

Super-resolution (SR) is a technique that produces a high resolution (HR) image via employing a number of low resolution (LR) images from the same scene. One of the degradations that attenuates performance of the SR is the blurriness of the input LR images. In many previous works in the SR, the blurriness of the LR images is assumed to be due to the integral effect of the image sensor of the im...

In the present paper we present the tensor-product approximation of multidimensional convolution transform discretized via collocation-projection scheme on the uniform or composite refined grids. Examples of convolving kernels are given by the classical Newton, Slater (exponential) and Yukawa potentials, 1/‖x‖, e−λ‖x‖ and e−λ‖x‖/‖x‖ with x ∈ Rd. For piecewise constant elements on the uniform gr...

In this paper, we present classes of kernels for machine learning from a statistics perspective. Indeed, kernels are positive definite functions and thus also covariances. After discussing key properties of kernels, as well as a new formula to construct kernels, we present several important classes of kernels: anisotropic stationary kernels, isotropic stationary kernels, compactly supported ker...

With the emergence of passive and active optical sensors available for geospatial imaging, information fusion across sensors is becoming ever more important. An important aspect of single (or multiple) sensor geospatial image analysis is feature extraction — the process of finding “optimal” lower dimensional subspaces that adequately characterize class-specific information for subsequent analys...

A consistent microscopic theory for the response of strongly coupled superfluid fermionic systems is formulated. After defining as a two-point two-fermion correlation function in basis Bogolyubov quasiparticles, equation motion (EOM) method applied using most general Hamiltonian with bare two-body interaction, also transformed to quasiparticle space. As extension case normal phase, resulting EO...

Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear unstructured/native grids. Version 2.1 the TE now provides extensive support examining both nodal (i.e., pointwise) areal features, including tropical extratropical cyclones, monsoonal lows depressions, atmospheric ...