Abstract.
The adaptive processing of structured information (e.g. sequences,
trees, graphs) is an emerging and critical topic in current Machine
Learning research. In this thesis we propose the study of methods
that are based on recursive functions for the adaptive transduction
from structured domains, basically in the area of Neural Networks.
Specifically, the neural computing realization of this approach, i.e.
Recursive Neural Networks, deals with prediction tasks for patterns
belonging to a structured domain, allowing a combination of the
flexibility and the robustness of connectionist models with the
representational power of a structured domain in a learning system.
Exploiting this basic idea, we propose new models, analysis of
theoretical properties and applications aimed at extending the
potentialities of the recursive neural computing approach in the
Machine Learning framework.
A family of models, based on the Recursive Cascade Correlation
methodology, is introduced to deal with contextual information in
structured domains, allowing the extension of the traditional
causality assumption. The proposed models allow us to realize
contextual transductions and to extend, with respect to the causal
models, the class of functions that can be computed for direct acyclic
positional graphs. The computational power of the new contextual
models is assessed both theoretically and experimentally.
The extension of the recursive dynamics to the unsupervised learning
paradigm allows the formulation of a general framework for
self-organizing maps able to cover previous approaches in
literature. We offer a uniform formalism for studying the training
mechanisms, the theoretical properties and the extensions to
alternative new recursive-based unsupervised methods.
Finally, the adaptive recursive approach is exploited to develop a
novel methodology for scientific applications in the area of
computational Chemistry, offering a new perspective to the research in
drug design and in the discovery of new compounds. The aim is to
develop a general and flexible method for QSPR/QSAR (Quantitative
Structure-Property Relationship and Quantitative Structure-Activity
Relationship) analysis and to assess the recursive approach in
real-world applications. Experimental results show the efficacity of
the proposed approach for coping with heterogeneous data and problems.
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