代码搜索：Regularization

找到约 355 项符合「Regularization」的源代码

代码结果 355
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www.eeworm.com/read/331336/12832487

m leaveoneout_lssvm.m

function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct) % Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion % % >> cost = leaveoneout_lssvm({X,Y,typ
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www.eeworm.com/read/139320/13161372

m leaveoneout_lssvm.m

function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct) % Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion % % >> cost = leaveoneout_lssvm({X,Y,typ
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www.eeworm.com/read/324303/13273731

m leaveoneout_lssvm.m

function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct) % Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion % % >> cost = leaveoneout_lssvm({X,Y,typ
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www.eeworm.com/read/137285/13334889

nn_var

# neural net variables # # file to be read by commander.p # each line is turned into a structure entry (str) # a default-setting entry (def) # a usage-printing entry (usg) # and a co
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www.eeworm.com/read/137285/13334923

c nn_var_usg.c

DNT; fprintf( fp, "-nnr report (reporting style )", nc->report); DNT; fprintf( fp, "-nnv verbose (verbosity 0/1/2 )", nc->verbose); DNT; fprintf( fp,
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www.eeworm.com/read/137285/13335042

h var_str.h

int train_n ; /* number to train on */ char infile[100] ;/* weights from (instead of default) */ int init_rule ; /* how to init wts */ int train ; /* wh
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www.eeworm.com/read/318947/13465978

m leaveoneout_lssvm.m

function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct) % Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion % % >> cost = leaveoneout_lssvm({X,Y,typ
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www.eeworm.com/read/316944/13514011

m leaveoneout_lssvm.m

function [costs, z, yh, model] = leaveoneout_lssvm(model,gams, estfct) % Fast leave-one-out cross-validation for the LS-SVM based on one full matrix inversion % % >> cost = leaveoneout_lssvm({X,Y,typ
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www.eeworm.com/read/400577/11573349

m linearr.m

%LINEARR Linear regression % % Y = LINEARR(X,LAMBDA,N) % % INPUT % X Dataset % LAMBDA Regularization parameter (default: no regularization) % N Order of polynomial (optional) %
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www.eeworm.com/read/341346/12090115

m gcv.m

function [rpar, G] = gcv(U, s, g, method) % GCV Generalized cross-validation.广义交叉校验 % % Given a matrix of left singular vectors U, a vector of singular % values s, a data vector g, and a re
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