| Acronym: DeLKeTI |
| Main Objective: 1. Development of techniques to approximate the universal distance for sequences and multidimensional data; this will lead to an approximate universal kernel. Using the ability of compression algorithms to approximate the universal distance, kernels will be tailored to each particular problem by exploiting different characteristics of the data. 2. Application of universal kernels in learning algorithms such as support vector machines or kernel logistic regression. 3. Theoretical characterization of the concentration properties (bounds) of the approximations to the universal distance, namely by exploiting known connection to Shannon's information. 4. Development of kernel learning techniques, in which the distance measure is learned from data (in supervised, semi-supervised, or non-supervised modes), rather than specified a priori. 5. Application of the developed kernels to problems in text and image analysis; e.g., categorization, desambiguation, clustering. |
| Reference: PTDC/EEA-TEL/72572/2006 |
| Funding: FCT/PTDC |
| Start Date: 01-11-2007 |
| End Date: 01-06-2011 |
| Team: Mario Alexandre Teles de Figueiredo, José David Pereira Coutinho Gomes Antão |
| Groups: Pattern and Image Analysis – Lx |
| Partners: Priberam Informática, SA |
| Local Coordinator: Mario Alexandre Teles de Figueiredo |
| Links: Internal Page |
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Associated Publications
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