代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/131588/14136409
m rce.m
function D = RCE(train_features, train_targets, lambda_m, region)
% Classify using the reduced coulomb energy algorithm
% Inputs:
% features - Train features
% targets - Train targets
% la
www.eeworm.com/read/131588/14136435
m stumps.m
function [D, w] = Stumps(train_features, train_targets, params, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% weights -
www.eeworm.com/read/129915/14217624
m minimum_cost.m
function D = Minimum_Cost(train_features, train_targets, lambda, region)
% Classify using the minimum error criterion via histogram estimation of the densities
% Inputs:
% features- Train featur
www.eeworm.com/read/129915/14217655
m optimal_brain_surgeon.m
function [D, Wh, Wo] = Optimal_Brain_Surgeon(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm and remove excess units
% usi
www.eeworm.com/read/129915/14217680
m perceptron.m
function D = Perceptron(train_features, train_targets, alg_param, region)
% Classify using the Perceptron algorithm (Fixed increment single-sample perceptron)
% Inputs:
% features - Train featur
www.eeworm.com/read/129915/14217691
m projection_pursuit.m
function [D, V, Wo] = Projection_Pursuit(train_features, train_targets, Ncomponents, region)
% Classify using projection pursuit regression
% Inputs:
% features- Train features
% targets - Trai
www.eeworm.com/read/129915/14217750
m balanced_winnow.m
function [D, a_plus, a_minus] = Balanced_Winnow(train_features, train_targets, params, region)
% Classify using the balanced Winnow algorithm
% Inputs:
% features - Train features
% targets
www.eeworm.com/read/129915/14217784
m rce.m
function D = RCE(train_features, train_targets, lambda_m, region)
% Classify using the reduced coulomb energy algorithm
% Inputs:
% features - Train features
% targets - Train targets
% la
www.eeworm.com/read/129915/14217800
m stumps.m
function [D, w] = Stumps(train_features, train_targets, params, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% weights -
www.eeworm.com/read/38039/1092238
mnu classifyloop.mnu
CLASSIFY#LOOP 环分类
#
Inner 内侧
The loop is inner (intended for filling).
环在内侧（用于填充）。
Outer 外侧
The loop is outer (intended for extension).
环在外侧（用于延伸）。