Download Constructive Neural Networks by Maria do Carmo Nicoletti, João R. Bertini Jr. (auth.), PDF

By Maria do Carmo Nicoletti, João R. Bertini Jr. (auth.), Leonardo Franco, David A. Elizondo, José M. Jerez (eds.)

The booklet is a set of invited papers on optimistic tools for Neural networks. many of the chapters are prolonged types of works offered at the particular consultation on optimistic neural community algorithms of the 18th foreign convention on synthetic Neural Networks (ICANN 2008) held September 3-6, 2008 in Prague, Czech Republic.

The publication is dedicated to confident neural networks and different incremental studying algorithms that represent an alternative choice to regular trial and mistake equipment for looking sufficient architectures. it really is made from 15 articles which offer an summary of the latest advances at the ideas being built for optimistic neural networks and their functions. it will likely be of curiosity to researchers in and teachers and to post-graduate scholars attracted to the newest advances and advancements within the box of man-made neural networks.

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U r A N D ... S w itc h . . z m L a ttic iz e r L a ttic iz e r x . . x d x 1 x 2 . . x d Fig. 2 The schema of a Switching Neural Network inserted on (1)) for each output value y and in properly combining the functions relative to the different output classes. To this aim, each generated implicant can be characterized by a weight wh > 0, which measures its significance level for the examples in the training set. Thus, to each Boolean string z can be assigned a weight vector u whose h-th component is uh = Fh (z) = wh if j∈P(ah ) z j = 1 0 otherwise where ah ∈ A0 ∪ A1 for h = 1, .

3 An Approximate Method for Solving the LP Problem The solution of the problems (4) (or (6)) and (7) (or (8)) allows the generation of a minimal set of implicants for the problem at hand. Nevertheless, in the presence of a large amount of data, the number of variables and constraints for the LP problems increases considerably, thus making the SP algorithm very slow. Efficient Constructive Techniques for Training Switching Neural Networks 41 Conversion of the continuous solution into a binary vector 1 Set ai = 0 for each i = 1, .

Notice that the minimization of (4) or (6) is obtained using the package Gnu Linear Programming Kit (GLPK) [8], a free library for the solution of linear programming problems. It is thus possible to improve the above results by adopting more efficient tools to solve the LP problem for the generation of implicants. Concluding Remarks In this paper a general schema for constructive methods has been presented and employed to train a Switching Neural Network (SNN), a novel connectionist model for the solution of classification problems.

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