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Your search keyword '"Alippi, Cesare"' showing total 44 results
Start Over Author "Alippi, Cesare" Publication Type Books
44 results on '"Alippi, Cesare"'

1. Contributors

Author

Alippi, Cesare, primary, Brown, David G., additional, Campolo, Maurizio, additional, Choe, Yoonsuck, additional, Duffy, Nigel, additional, Érdi, Péter, additional, Fink, Dan, additional, Francon, Olivier, additional, Galdi, Paola, additional, Gonzalez, Santiago, additional, Gori, Marco, additional, Grossberg, Stephen, additional, Hodjat, Babak, additional, Ieracitano, Cosimo, additional, Kant, Mohak, additional, Kasabov, Nikola Kirilov, additional, Kim, Youngsik, additional, Kozma, Robert, additional, Levine, Daniel S., additional, Li, Qinbo, additional, Liang, Jason, additional, Maggini, Marco, additional, Mammone, Nadia, additional, Meyerson, Elliot, additional, Miikkulainen, Risto, additional, Morabito, Francesco Carlo, additional, Navruzyan, Arshak, additional, Ormandy, Roman, additional, Ozawa, Seiichi, additional, Park, Dookun, additional, Perin, Jose Krause, additional, Raju, Bala, additional, Rawal, Aditya, additional, Samuelson, Frank W., additional, Serra, Angela, additional, Shahrzad, Hormoz, additional, Tagliaferri, Roberto, additional, Tiezzi, Matteo, additional, Werbos, Paul J., additional, and Widrow, Bernard, additional

Published
2024
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38. Artificial intelligence in the age of neural networks and brain computing.

Author

Alippi, Cesare, Choe, Yoonsuck, Kozma, Robert, and Morabito, F. C.

Subjects
Artificial intelligence, Brain-computer interfaces, Neural networks (Computer science)
Abstract

Summary: Artificial Intelligence in the Age of Neural Networks and Brain Computing is the comprehensive guide for neural network advances in artificial intelligence (AI). It covers the major, basic ideas of "brain-like computing" behind AI, providing a framework to deep learning and launching novel and intriguing paradigms as possible future alternatives. Following an introduction, initial chapters discuss revolutionary new brain-mind approaches alternative to deep learning, the brain-mind-computer trichotomy, pitfalls and opportunities in the development of AI systems. Subsequent chapters explore a deep learning approach to electrophysiological multivariate time series analysis, multiview learning in biomedical applications, and the evolution of deep neural networks. This is an essential companion to researchers, engineers, advance AI practitioners, postdoctoral students in computational intelligence and neural engineering, and the technically oriented public. It provides access to the latest up-to-date knowledge from top, global experts working on theory and cutting-edge applications in signal processing, speech recognition, games, adaptive control, and decision-making. -- From back cover.

Published
2019

44. Adaptive Classifiers with ICI-Based Adaptive Knowledge Base Management.

Author

Alippi, Cesare, Boracchi, Giacomo, and Roveri, Manuel

Abstract

Classification systems meant to operate in non-stationary environments are requested to adapt when the process generating the observed data changes. A particularly effective form of adaptation in the abrupt perturbation case suggests to release the obsolete knowledge base of the classifier (or training set), and consider novel samples to configure the new classification model. In this direction, we propose an adaptive classifier based on a change detection test used both for detecting changes in the process and identifying the new training set (and, then, the new classifier). A key point of the proposed solution is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process generating the data evolves through a sequence of abrupt changes. [ABSTRACT FROM AUTHOR]

Published
2010
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