Intelligent Sensorial Processing
Objectives
To know:
- Automatic classifiers design
- Automatic classifiers performance evaluation
- Applications of decision support systems
To do:
- Development of automatic classifiers
Integration of decision support systems
General characterization
Code
10964
Credits
6.0
Responsible teacher
José Manuel Matos Ribeiro da Fonseca
Hours
Weekly - 4
Total - 56
Teaching language
Português
Prerequisites
Available soon
Bibliography
Leo Breiman, Jerome Friedman, Charles J. Stone, R.A. Olshen. Classification and Regression Trees. Chapman and Hall, New York.
Quinlan, Ross J. (1993). C4.5: Programs for Machine Learning. San Mateo, California. Morgan Kaufmann.
Weiss S. M., Kulikowski C. A. (1991) Computer systems that learn. San Mateo, California. Morgan Kaufmann.
Fonseca, José M. (1994). Indução de Árvores de Decisão. Tese de Mestrado
Teaching method
Available soon
Evaluation method
Available soon
Subject matter
• Introduction to automatic classifiers design
• Estimation of a classifier quality
• Notion of error and cost
• Methods for estimating the error of classifiers.
• Nearest-neighbor classifiers.
• Decision trees.
• Methods for the partition of discrete and continuous characteristics
• Criteria for selection of attributes
• Methods for limiting the growth of decision trees
• Decision trees forests
• Neural networks.
• The perceptron
• Back-propagation
• Multi-layer neural networks
• Classifiers based on genetic algorithms.
• Fuzzy classifiers
• Principles of fuzzy controllers
• Fuzzy inference
• Fuzzy Algebra
• Biometric techniques
• Identification systems based on biometrics: iris recognition, retina, hand, fingerprint, face, etc.