Research topics

  • Heterogeneous Neural Networks
The topic of my Ph.D. Thesis, which I finished in 2000. These networks are special classes of functions based on generalized neuron models. These models are cast in the common framework of computing an explicit similarity function and can naturally handle heterogeneous information such as categorical, ordinal or fuzzy data, as well as missing values. Then the neurons can be used to create artificial neural networks.

» I am still working in new training methods, as well as further generalizations and developments.

  • Feature Selection Algorithms
 These are algorithms that try to find a reduced representation of a problem description, with less features than the original. In this new representation redundant, irrelevant, poorly relevant or highly noisy features have (hopefully) been removed. This may bring benefits as better generalization (less overfit), faster computation and gained domain knowledge.

» I am interested in
both theoretical developments (which are lacking in the literature) and in sensible experimental comparisons
  • Kernel functions
These are one of the key ingredients of kernel-based methods (such as support vector machines). One of the fascinating aspects of these methods is that they can be applied to almost any kind of data without distorting codings, if one is able to develop a valid kernel function.

» I am specially interested in the design of kernels capable of handling non-standard data (categorical, fuzzy, ...) as well as missing values.

  • Similarity relations
These are two-place functions very commonly used in data analysis and strongly related to dissimilarity functions and metrics.

» I am interested in developing an axiomatic formalization, as well as a body of results than can both characterise the two sets of functions, their relation and their properties.

» A long-term and (probably very ambitious) goal is the the development of methods to relate a data set with a promising similarity.

  • Breeder Genetic Algorithms
 The Breeder Genetic Algorithm (BGA) is in midway between Genetic Algorithms and Evolution Strategies. It works directly in a continuous space and is mainly recombination driven, though mutation has also a place. It keeps a good compromise between simplicity and effectiveness. I developed new genetic operators for the BGA and was interested in comparisons with other methods (GAs, backprop, ...) to train multilayer perceptrons.

» I have discontinued research on this topic although I keep a general interest in evolutionary algorithms, specially in the rather new CMA-ES method.
  • Applications 
I have always tried to find opportunities for collaboration with people having data (and a specific data analysis problem). This brings many benefits: you meet people with different background, you can apply your methods on real data and you have an expert that guides the analysis. Through the years I have been lucky enough to meet scientists at the University of Girona (wastewater treatment plant data) and the University of Barcelona (microbiology data), as well as local industries (e.g., insullation panels) as  well as hospitals (e.g., brain tumours).

» I am still very interested in this applied side of the work.

For a complete list of papers, please have a look at this page.


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