代码搜索：learner
找到约 833 项符合「learner」的源代码
代码结果 833
www.eeworm.com/read/280595/10311915
m adaboost.m
function model = adaboost(data,options)
% ADABOOST AdaBoost algorithm.
%
% Synopsis:
% model = adaboost(data,options)
%
% Description:
% This function implements the AdaBoost algorithm which
% prod
www.eeworm.com/read/299459/7850468
m adaboost.m
function model = adaboost(data,options)
% ADABOOST AdaBoost algorithm.
%
% Synopsis:
% model = adaboost(data,options)
%
% Description:
% This function implements the AdaBoost algorithm which
% prod
www.eeworm.com/read/312163/13617455
m adaboost.m
function model = adaboost(data,options)
% ADABOOST AdaBoost algorithm.
%
% Synopsis:
% model = adaboost(data,options)
%
% Description:
% This function implements the AdaBoost algorithm which
% prod
www.eeworm.com/read/130196/5963108
m do_learn.m
function lrn=do_learn(lrn, dataset)
% lrn=learner.do_learn(lrn, dataset)
%
% parameter class
% lrn learner
% dataset data
% G. Raetsch 1.6.98
% Copyright (c) 1998 GMD Berlin - All ri
www.eeworm.com/read/130196/5963116
m display.m
function display(rn)
%
% G. Raetsch 1.6.98
% Copyright (c) 1998 GMD Berlin - All rights reserved
% THIS IS UNPUBLISHED PROPRIETARY SOURCE CODE of GMD FIRST Berlin
% The copyright notic
www.eeworm.com/read/150760/12265818
m adaboost.m
function model = adaboost(data,options)
% ADABOOST AdaBoost algorithm.
%
% Synopsis:
% model = adaboost(data,options)
%
% Description:
% This function implements the AdaBoost algorithm which
% prod
www.eeworm.com/read/213492/15133687
m adaboost.m
function model = adaboost(data,options)
% ADABOOST AdaBoost algorithm.
%
% Synopsis:
% model = adaboost(data,options)
%
% Description:
% This function implements the AdaBoost algorithm which
% prod
www.eeworm.com/read/411674/11233775
m adaboost.m
function model = adaboost(data,options)
% ADABOOST AdaBoost algorithm.
%
% Synopsis:
% model = adaboost(data,options)
%
% Description:
% This function implements the AdaBoost algorithm which
% prod
www.eeworm.com/read/227593/4773961
pm decisiontree.pm
package AI::Categorizer::Learner::DecisionTree;
$VERSION = '0.01';
use strict;
use AI::DecisionTree;
use AI::Categorizer::Learner::Boolean;
use base qw(AI::Categorizer::Learner::Boolean);
sub create
www.eeworm.com/read/429426/1948677
py thresholding1.py
import orange, orngWrap, orngTest, orngStat
data = orange.ExampleTable("bupa")
learner = orange.BayesLearner()
thresh = orngWrap.ThresholdLearner(learner = learner)
thresh80 = orngWrap.Thresho