Optimization under uncertainty and risk is ubiquitous in business, engineering, finance. Typically, we use observed or simulated data our decision models, which aim to control risk, result composite functionals. The paper addresses the stability of problems when functionals are subjected measure perturbations at multiple levels potentially different nature. We analyze data-driven formulations w...

We introduce several new families of string kernels designed in particular for use with support vector machines (SVMs) for classification of protein sequence data. These kernels – restricted gappy kernels, substitution kernels, and wildcard kernels – are based on feature spaces indexed by k-length subsequences from the string alphabet Σ (or the alphabet augmented by a wildcard character), and h...

We describe several families of k-mer based string kernels related to the recently presented mismatch kernel and designed for use with support vector machines (SVMs) for classification of protein sequence data. These new kernels – restricted gappy kernels, substitution kernels, and wildcard kernels – are based on feature spaces indexed by k-length subsequences (“k-mers”) from the string alphabe...

w L r w h c w h i a i d m t n ( e S t s u ( t r p r Individual kernels of wheat containing immature ice weevils, Sitophilus oryzae (L.) (Coleoptera: Curcuionidae), parasitized by Anisopteromalus calandrae Howard) (Hymenoptera: Pteromalidae) could be sepaated from uninfested kernels and kernels that conained unparasitized weevils by using near-infrared NIR) spectroscopy. Wheat kernels from cultu...

In this paper a correspondence is derived between regularization operators used in regularization networks and support vector kernels. We prove that the Green's Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties. Moreover, the paper provides an analysis of currently used support vector kernels in the view of regulariz...

In this paper, we propose a family of kernels for the data distributions belonging to the exponential family. We call these kernels generative kernels because they take into account the generative process of the data. Our proposed method considers the geometry of the data distribution to build a set of efficient closed-form kernels best suited for that distribution. We compare our generative ke...

The term “CoRE kernel” stands for correlationresemblance kernel. In many real-world applications (e.g., computer vision), the data are often high-dimensional, sparse, and non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary sparse data and demonstrate the effectiveness of the new kernels through a classification experiment. CoRE kernels are simple with no tuning parameter...

Sensitivity kernels for wave equation based migration velocity analysis in local angle-domain are formulated. These sensitivity kernels serve as operators to backproject the residual moveout in local angle-domain common image gathers to migration models for velocity updating. To validate the theoretical formulation, we directly measure sensitivity kernels using a perturb-measuring approach. For...

Kernels at the ear tip of field grown maize (Zea mays L.) often abort during flowering, resulting in significant yield loss. The objective of this study was to determine if abortion is initiated by an inadequate supply of carbohydrates for growth of ear tip kernels, and/or by a hormonal signal. Field grown maize plants were either unshaded or shaded during flowering to increase kernel abortion....

An extension for univariate kernels that deals with missing values is proposed. These extended kernels are shown to be valid Mercer kernels and can adapt to many types of variables, such as categorical or continuous. The proposed kernels are tested against standard RBF kernels in a variety of benchmark problems showing different amounts of missing values and variable types. Our experimental res...