Kernel-based data mining algorithms, such as Support Vector Machines, project data into high-dimensional feature spaces, wherein linear decision surfaces correspond to non-linear decision surfaces in the original feature space. Choosing a kernel amounts to choosing a high-dimensional feature space, and is thus a crucial step in the data mining process. Despite this fact, and as a result of the ...

We propose a composite realized kernel to estimate the ex-post covariation of asset prices. Composite realized kernels are a data efficient method where the covariance estimate is composed of univariate realized kernels to estimate variances and bivariate realized kernels to estimate correlations. We analyze the merits of our composite realized kernels in an ultra high dimensional environment, ...

Determining protein function constitutes an exercise in integrating information derived from several heterogeneous high-throughput experiments. To utilize the information spread across multiple sources in a combined fashion, these data sources are transformed into kernels. Several protein function prediction methods follow a two-phased approach: they first optimize the weights on individual ker...

In this paper we propose a novel evolutionary algorithm for regularization networks. The main drawback of regularization networks in practical applications is the presence of meta-parameters, including the type and parameters of kernel functions Our learning algorithm provides a solution to this problem by searching through a space of different kernel functions, including sum and composite kern...

The automatic extraction of relations between entities expressed in natural language text is an important problem for IR and text understanding. In this paper we show how different kernels for parse trees can be combined to improve the relation extraction quality. On a public benchmark dataset the combination of a kernel for phrase grammar parse trees and for dependency parse trees outperforms ...

The clinical narrative contains a great deal of valuable information that is only understandable in a temporal context. Events, time expressions, and temporal relations convey information about the time course of a patient’s clinical record that must be understood for many applications of interest. In this paper, we focus on extracting information about how time expressions and events are relat...

Detection of drug-drug interaction (DDI) is crucial for identification of adverse drug effects. In this paper, we present a range of new composite kernels that are evaluated in the DDIExtraction2011 challenge. These kernels are computed using different combinations of tree and feature based kernels. The best result that we obtained is an F1 score of 0.6370 which is higher than the already publi...

The classification of remote sensing images has done great forward taking into account the image’s availability with different resolutions, as well as an abundance of very efficient classification algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support Vector Machines (SVM) which are a group of supervised classification algorith...