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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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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