# The arboretum procedure

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 : documentationdocumentation -> From cyber-crime to insider trading, digital investigators are increasingly being asked todocumentation -> EnCase Forensic Transform Your Investigationsdocumentation -> File Sharing Documentation Prepared by Alan Halter Created: 1/7/2016 Modified: 1/7/2016documentation -> Gaia Data Release 1 Documentation release 0 Mbernoulli Yes Yes No Multinomial Yes Yes No Mentropy Yes Yes No RANDF=number Specifies the degrees of freedom parameter for random numbers. See the following Randomization Options and Default Parameters table. RANDIST=name Specifies the type of distribution to be used for random initial weights and perturbations. The distributions and default parameter values are as follows: Randomization Options and Default Parameters RANDIST RANLOC RANSCALE DF NORMAL mean=0 std=1 - UNIFORM mean=0 halfrange=1 - CAUCHY median=0 scale=1 - CHIINV - scale=1 df=1 Default: NORMAL RANDOM=integer Specifies the random number seed. Default: 0 RANLOC=number Specifies the location parameter for random numbers. See the above Randomization Options and Default Parameters table. Specifies the scale parameter for random numbers. See the above Randomization Options and Default Parameters table. Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. The NEURAL Procedure NLOPTIONS Statement Identifies the nonlinear optimization options to set. Category Option Statement - does not directly affect the network, but sets options for use in subsequent action statements. The options persist until reset at a later stage in the processing. NLOPTIONS ; Nonlinear Options ABSCONV= number Specifies an absolute function convergence criterion. ABSCONV= is a function of the log-likelihood for the intercept-only model. Default: The default value is the negative square root of the largest double precision value. Range:___number'>Range: number > 0 ABSFCONV= number Specifies an absolute function convergence criterion. Default: 0 Range: number > 0 ABSGCONV= number Specifies the absolute gradient convergence criterion. Default: 1E-5 Range: number > 0 ABSXCONV= number Specifies the absolute parameter convergence criterion. Default: 0 Range: number > 0 DAMPSTEP= number Specifies that the initial step size value for each line search used by the QUANEW, CONGRA, or NEWRAP optimization technique cannot be larger than the product of number and the step size value used in the former iteration. Default: 2 Range: number > 0 DIAHES Forces the optimization algorithm (TRUREG, NEWRAP, or NRRIDG) to take advantage of the diagonality. FCONV= number Specifies a function convergence criterion. Default: 1E-4 Range: number > 0 FSIZE= number Specifies the parameter of the relative function and relative gradient termination criteria. Default: Not applicable. Range: number   0 GCONV= number Specifies the relative gradient convergence criterion. Default: 1E-8 Range: number > 0 HESCAL= 0 | 1 | 2 |3 Specifies the scaling version of the Hessian or cross-product Jacobian matrix used in NRRIDG, TRUREG, LEVMAR, NEWRAP, or DBLDOG optimization. Default: 0 - for all others INHESSIAN= number Specifies how to define the initial estimate of the approximate Hessian for the quasi-Newton techniques QUANEW and DBLDOG. Default: The default is to use a Hessian based on the initial weights as the initial estimate of the approximate Hessian. When r=0, the initial estimate of the approximate Hessian is computed from the magnitude of the initial gradient. Range: number > 0 INSTEP= number Specifies the initial radius of the trust region used in the TRUREG, DBLDOG, and LEVMAR algorithms. Default: 1 Range: number > 0 LCEPS | LCEPSILON= number Specifies the range for active constraints. Range: number > 0 LCSINGULAR= number Specifies the tolerance for dependent constraints Range: number > 0 LINESEARCH= number Specifies the line-search method for the CONGRA, QUANEW, and NEWRAP optimization techniques. Default: 2 Range: 1   number   8 LSPRECISION= number Specifies the degree of accuracy that should be obtained by the second and third line-search algorithms. Dostları ilə paylaş:

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