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Integrated Architectures for Machine Learning

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Machine Learning and Its Applications (ACAI 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2049))

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Abstract

With the growing complexity of Machine Learning applications, the need for using integrated or hybrid (or multistrategy) approaches becomes more and more imperative, and an increasing amount of research effort is devoted to this issue. The increasing complexity of applications is not the only reason making multistrategic approaches appealing: as it is well known, no single approach/system can claim to be uniformly superior to any other, so that hybridisation seems a natural and viable way of compensating drawbacks and enhancing advantages. Even though there is no common agreement on what integration exactly means in Machine Learning, in a broad sense an integrated architecture can be defined as one which is organised or structured so that its constituent units function co-operatively.

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  1. Dipartimento di Scienze e Tecnologie Avanzate, Università del Piemonte Orientale, Corso Borsalino 54, 15100, Alessandria, Italy

    Lorenza Saitta

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  1. Lorenza Saitta
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  1. National Centre for Scientific Research “Demokritos”, Institute of Informatics and Telecommunications, P.O. Box 60228, Ag. Paraskevi, 15310, Athens, Greece

    Georgios Paliouras , Vangelis Karkaletsis  & Constantine D. Spyropoulos ,  & 

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Saitta, L. (2001). Integrated Architectures for Machine Learning. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_10

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  • DOI: https://doi.org/10.1007/3-540-44673-7_10

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