The advanced level of today's research in Computational Intelligence makes the practical applications of such methods feasible. Research at SeaLab has been covering this area for more than ten years. The results obtained can be arranged within the following macro categories:
Research in this area mainly concerns innovative algorithms for intelligent data processing. Classification and unsupervised data representation are the basic areas of interest. The basic goal always is to develop algorithms that can prove effective in real-world applications; therefore a major accent is posed on the characterization of the expected performance, thereby involving generalization and validation issues. The research in this area lead to the formulation of some novel models of Connectionist Computing:
The following table summarizes the various applications that have been experienced for the above-mentioned models, typically including high-dimensional intelligent signal processing:
Methods and Theoretical Models |
Applications |
Connectionist Models for intelligent signal processing
Circular Back-Propagation Networks |
Image-quality prediction
Industrial process control and pollutant-emission forecasting Clinical diagnosis Object visual tracking |
Connectionist Models for data clustering and mining
Vector Quantization |
Network Security
License-plate localization Optical Character Recognition Novelty detection Text Mining for (business) intelligence |
Support Vector Machines for classification |
Image-quality prediction
Data mining, novelty detection Security Risk-assessment scenarios |
Advanced Computational Paradigms
Quantum Computing Optimization |
Advanced methods for optimal engineering design |
The research on applied Quantum Computing aims
at exploiting Quantum Parallelism in solving highly complex design problems
in engineering.
The ability of Quantum Computers to handle an exponential amount of information
using a linear amount of resources allows one to tackle optimization task
that otherwise would only involve empirical, sub-optimal approaches. The research
has applied Quantum Optimization methods for the optimal training of several
paradigms from Computational Intelligence, namely, quantum optimization for
Support Vector Machines and Quantum training of neural Vector Quantizers.