Kernel-based machine learning algorithms are versatile tools for biological sequence data analysis. Special sequence kernels can endow Support Vector Machines with biological knowledge to perform accurate classification of diverse sequence data. The kernels relative strengths and weaknesses are difficult to evaluate on single data sets. We examine a range of recent kernels tailor-made for biolo...

Kernel combination is meant to improve the performance of single kernels and avoid the difficulty of kernel selection. The most common way of combining kernels is to compute their weighted sum. Usually, the kernels are assumed to exist in independent empirical feature spaces and therefore were combined without considering their relationships. To take these relationships into consideration in ke...

We present a new approach to construct kernels used on support vector machines for speaker verification. The idea is to learn new kernels by taking linear combination of many kernels such as the Generalized Linear Discriminant Sequence kernels (GLDS) and Gaussian Mixture Models (GMM) supervector kernels. In this new linear kernel combination, the weights are speaker dependent rather than univer...

Abstract. To achieve sparse description that allows intuitive analysis, we aim to represent deformation with a basis containing interpretable elements, and we wish to use elements that have the description capacity to represent the deformation compactly. We accomplish this by introducing higher order kernels in the LDDMM registration framework. The kernels allow local description of affine tran...

This paper presents a new class of matrix valued kernels, which are ideally suited to learn vector valued equivariant functions. Matrix valued kernels are a natural generalization of the common notion of a kernel. We set the theoretical foundations of so called equivariant matrix valued kernels. We work out several properties of equivariant kernels, we give an interpretation of their behavior a...

kProlog is a simple algebraic extension of Prolog with facts and rules annotated with semiring labels. We propose kProlog as a language for learning with kernels. kProlog allows to elegantly specify systems of algebraic expressions on databases. We propose some code examples of gradually increasing complexity, we give a declarative specification of some matrix operations and an algorithm to sol...

This paper devises a novel kernel function for structured natural language data. In the field of Natural Language Processing, feature extraction consists of the following two steps: (1) syntactically and semantically analyzing raw data, i.e., character strings, then representing the results as discrete structures, such as parse trees and dependency graphs with part-of-speech tags; (2) creating ...

We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on a innnite set from kernels involving generators of the set. The family of kernels generated generalizes the family of radial basis kernels. It can also be used to deene kernels in the form of joint Gibbs proba...

Convolution kernels for trees provide effective means for learning with treestructured data, such as parse trees of natural language sentences. Unfortunately, the computation time of tree kernels is quadratic in the size of the trees as all pairs of nodes need to be compared: large trees render convolution kernels inapplicable. In this paper, we propose a simple but efficient approximation tech...

In this paper we present a choreography that explains the process of supervised machine learning. We present how a perceptron (in its dual form) uses convolution kernels to learn to differentiate between two categories of objects. Convolution kernels such as string kernels and tree kernels are widely used in Natural Language Processing (NLP) applications. However, the baggage associated with le...