Kernel Methods for Structured Data

Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel generalizes dot products to arbitrary domains and can thus be seen as a similarity measure between data points with complex structures. The use of kernels allows to decouple the representation of the data from the specific learning algorithm, provided it can be defined in terms of distance or similarity between instances. Under this unifying formalism a wide range of methods have been developed, dealing with binary and multiclass classification, regression, ranking, clustering and novelty detection to name a few. Recent developments include statistical tests of dependency and alignments between related domains, such as documents written in different languages. Key to the success of any kernel method is the definition of an appropriate kernel for the data at hand. A well-designed kernel should capture the aspects characterizing similar instances while being computationally efficient. Building o...