There is a dramatic growth in the availability of complex data from a wide range of different applications. The challenge of the data analyzer is to extract knowledge from the raw... Watch Video

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The talk will consider ways of bounding the complexity of a graph as measured by the number of partitions satisfying certain properties. The approach adopted uses Vapnik Chervonenkis... Watch Video

We consider kernel learning for one-class Support Vector Machines. We consider a mix of 2- and 1-norms of the individual weight vector norms allowing control of the sparsity of the... Watch Video

Kernel methods have become a standard tool for pattern analysis during the last fifteen years since the introduction of support vector machines. We will introduce the key ideas and... Watch Video

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Many low Vapnik-Chervonenkis (and hence statistically learnable) classes cannot be represented as linear classes in such a way that they can be learnt with large margin approaches.... Watch Video

We apply methods of multiple kernel learning to the problem of system identification for multi-dimensional temporal data. Rather than building a full probabilistic model, we take a... Watch Video