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/135035/13966270
bp sonar.bp
* This program runs the aspect-angle dependent data from Gorman and
* Sejnowski's article: "Analysis of Hidden Units in a Layered Network
* Trained to Classify Sonar Targets", in Neural Networks, vo
www.eeworm.com/read/133394/14045009
m cart.m
function D = CART(train_features, train_targets, params, region)
% Classify using classification and regression trees
% Inputs:
% features - Train features
% targets - Train targets
% para
www.eeworm.com/read/191902/8417055
m nearestneighborediting.m
function D = NearestNeighborEditing(train_features, train_targets, params, region)
% Classify points using the nearest neighbor editing algorithm
% Inputs:
% train_features - Train features
% t
www.eeworm.com/read/191902/8417075
m perceptron_bvi.m
function D = Perceptron_BVI(train_features, train_targets, params, region)
% Classify using the batch variable increment Perceptron algorithm
% Inputs:
% features - Train features
% targets
www.eeworm.com/read/191902/8417116
m perceptron_batch.m
function D = Perceptron_Batch(train_features, train_targets, params, region)
% Classify using the batch Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets
www.eeworm.com/read/191902/8417244
m voted_perceptron.m
function D = voted_perceptron(train_features, train_targets, params, region);
% Classify using the Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Params
www.eeworm.com/read/191902/8417324
m lms.m
function D = LMS(train_features, train_targets, params, region)
% Classify using the least means square algorithm
% Inputs:
% features - Train features
% targets - Train targets
% param -
www.eeworm.com/read/191902/8417326
m backpropagation_cgd.m
function [D, Wh, Wo] = Backpropagation_CGD(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm and conjugate gradient descent
www.eeworm.com/read/191902/8417342
m backpropagation_sm.m
function [D, Wh, Wo] = Backpropagation_SM(train_features, train_targets, params, region)
% Classify using a backpropagation network with stochastic learning algorithm with momentum
% Inputs:
% f
www.eeworm.com/read/191902/8417372
m backpropagation_recurrent.m
function [D, Wh, Wo] = Backpropagation_Recurrent(train_features, train_targets, params, region)
% Classify using a backpropagation recurrent network with a batch learning algorithm
% Inputs:
% f