Bianchini M., Maggini M., Jain L.C. (eds.) Handbook on Neural Information Processing

Springer, 2013. — 546 p.This handbook is inspired by two fundamental questions: Can intelligent learning machines be built? and Can they be applied to face problems otherwise unsolvable?. A simple unique answer certainly does not exist to the first question. Instead in the last three decades, the great amount of research in machine learning has succeeded in answering many related, but far more specific, questions. In other words, many automatic tools able to learn in particular environments have been proposed. They do not show an intelligent behavior, in the human sense of the term, but certainly they can help in addressing problems that involve a deep perceptual understanding of such environments. Therefore, the answer to the second question is also partial and not fully satisfactory, even if a lot of challenging problems (computationally too hard to be faced in the classic algorithmic framework) can actually be tackled with machine learning techniques.
In this view, the handbook collects both well-established and new models in connectionism, together with their learning paradigms, and proposes a deep inspection of theoretical properties and advanced applications using a plain language, particularly tailored to non-experts. Not pretending to be exhaustive, this chapter and the whole book delineate an evolving picture of connectionism, in which neural information systems are moving towards approaches that try to keep most of the information unaltered and to specialize themselves, sometimes based on biological inspiration, to cope expertly with difficult real–world applications.Deep Learning of Representations
Recurrent Neural Networks
Supervised Neural Network Models for Processing Graphs
Topics on Cellular Neural Networks
Approximating Multivariable Functions by Feedforward Neural Nets
Bochner Integrals and Neural Networks
Semi-supervised Learning
Statistical Relational Learning
Kernel Methods for Structured Data
Multiple Classifier Systems: Theory, Applications and Tools
Self Organisation and Modal Learning: Algorithms and Applications
Bayesian Networks, Introduction and Practical Applications
Relevance Feedback in Content-Based Image Retrieval: A Survey
Learning Structural Representations of Text Documents in Large Document Collections
Neural Networks in Bioinformatics