Computational and Mathematical Modelling of the EGF Receptor System

This chapter gives an overview of computational and mathematical modelling of the EGF receptor system. It begins with a survey of motivations for producing such models, then describes the main approaches that are taken to carrying out such modelling, viz. differential equations and individual-based modelling. Finally, a number of projects that applying modelling and simulation techniques to various aspects of the EGF receptor system are described

Boolean Discriminant Functions in Symbolic Learning with Subclasses

Finding methods to increase the complexity of the Boolean discriminant functions and to stay within the limits of tractability set by combinatorics is an important task in the field of symbolic machine learning. The original formalism based on meta-features is introduced. Meta-features are predicates that describe relations between the features of the investigated objects and the subclasses (clusters inside classes) of the training set. The formalism facilitates finding Boolean discriminant functions of three variables. These are more complecated than simple conjunctions if the partition of the original training set into subclasses is given. The structure of meta-feature predicates is close to the structure of statements used by domain experts to describe their knowledge. Consequently, the formalism can be applied in hybrid learning systems, which incorporate information obtained from domain experts

Multi-feature construction based on genetic algorithms and non-algebraic feature representation to facilitate learning concepts with complex interactions

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 200