
TRAINING
Keyword of Integer
Type This keyword indicatea method that will be used to optimize
neural network weights. All these methods, with an exception of stiff and
LevenbergMarquardt algorithms, are heuristic algorithms that have a number
of adjustable parameters. These parameters were selected by the authors
of the algorithms to provide the fast convergence of the algorithms.
momentum {0}
 The simplest algorithm that started new interest in artificial neural
networks after article of Rumelhart et al., 1986. The momentum rate is
0.9 and eps is 0.2. SuperSAB {1}
 programmed according to Tollenaere, T. SuperSAB: Fast Adaptive Back
Propagation with Good Scaling Properties. Neural Networks. 1990, 3, 561573.
Momentum is 0.9, eps is 0.2, multiplication(+) is 1.05, multiplication()
is 0.5, max eps is 10. RPROP {2} 
Initial eps is 0.0001, minimal eps is 0.000001, multiplication(+) is 1.2,
multiplication() is 0.5. QuickProp {3}
 Falhman implementation updated from his original code by V.V. Kovalishyn.
Eps is 0.55, decay is 0.0001, minimal derive is 0.1, maximal momentum is
1.75. stiff {4}
Second order optimisation algorithm that converts the weights optimisation
to the problem of solution of the second order differential equations. QuickProp1 {5}
 Original implementation of I. V. Tetko inspired by the algorithm of
Falhman. The same parameters as in QuickProp are used. Marquardt {6}
 Second order LevenbergMarquardt optimisation algorithm (requires O(n2)
time, where n is the number of weights), see e.g. Shepherd, A. J. SecondOrder
Methods for Neural Networks; SpringerVerlag: London, 1997; 145. The default value is {1}, the SuperSAB algorithm.
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