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Abstract: . Learning from examples has a number of distinct algebraic forms, depending on what is to be learned from which available information. One of these forms is x G ! y, where the input--output tuple (x; y) is the available information, and G represents the process determining the mapping from x to y. Various models, y = f(x), of G can be constructed using the information from the (x; y) tuples. In general, and for real-world problems, it is not reasonable to expect the exact representation of... (Update)
Context of citations to this paper: More
...chooses between different experts. Methods include: ffl Voting [50] ffl Model averaging [52] ffl Committees [53] 54] ffl Agent teams [55]; ffl Stacked generalization [56] 57] ffl Monte Carlo methods [58] We used voting, the least sophisticated method of those listed. A...
...this can simply be the difference of values of the two highest active units. In regression, this requires a separate monitoring network [15] and a separate data set on which the monitor is trained. 2. These weights can be thought of as a second learning model combining...
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2: Neural Network Ensembles (context) - Hansen, Salamon - 1990
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BibTeX entry: (Update)
U. Beyer and F. J. Smieja, "Learning from examples, agent teams and the concept of reflection," Tech. Rep. REFLEX report 1993/1, GMD report 776, Institute for System Design Technology (GMD), Adaptive Systems group, Sankt Augustin, Germany, June 1993. http://citeseer.ist.psu.edu/beyer93learning.html More
@misc{ beyer93learning,
author = "U. Beyer and F. Smieja",
title = "Learning from examples, agent teams and the concept of reflection",
text = "U. Beyer and F. J. Smieja, Learning from examples, agent teams and the
concept of reflection, Tech. Rep. REFLEX report 1993/1, GMD report 776,
Institute for System Design Technology (GMD), Adaptive Systems group, Sankt
Augustin, Germany, June 1993.",
year = "1993",
url = "citeseer.ist.psu.edu/beyer93learning.html" }
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