Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …

This paper reviews and extends the field of similarity-based classification, presenting new
analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Most graph kernels are an instance of the class of R-Convolution kernels, which measure
the similarity of objects by comparing their substructures. Despite their empirical success …

We believe that the cluster assumption is key to successful semi-supervised learning. Based
on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances …

In this paper we propose a discriminant learning framework for problems in which data
consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we …

In this paper, we develop an approach to exploiting kernel methods with manifold-valued
data. In many computer vision problems, the data can be naturally represented as points on …

Abstract Symmetric Positive Definite (SPD) matrices have become popular to encode image
information. Accounting for the geometry of the Riemannian manifold of SPD matrices has …

The advantages of discriminative learning algorithms and kernel machines are combined
with generative modeling using a novel kernel between distributions. In the probability …

This paper presents a theoretical foundation for an SVM solver in Kreĭn spaces. Up to now,
all methods are based either on the matrix correction, or on non-convex minimization, or on …