Development and Learning of Kernels for Text and Images
| 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 |
| Name |
Development and Learning of Kernels for Text and Images |
| Funding |
FCT/PTDC |
| Start date |
01-11-2007 |
| Ending date |
01-06-2011 |
| Team |
Mário Alexandre Teles de Figueiredo ; José David Pereira Coutinho Gomes Antão ; André Martins |
| Groups |
Pattern and Image Analysis – Lx |
| Partners |
Priberam Informática, SA |
| Local coordinator |
Mário Alexandre Teles de Figueiredo |
| Other contributers |
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Project associated publications:
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