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/415311/11077211
m store_grabbag.m
function D = Store_Grabbag(train_features, train_targets, Knn, region)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% features - Train features
www.eeworm.com/read/415311/11077241
m pnn.m
function D = PNN(train_features, train_targets, sigma, region)
% Classify using a probabilistic neural network
% Inputs:
% features- Train features
% targets - Train targets
% sigma - Gaussi
www.eeworm.com/read/415311/11077268
m genetic_algorithm.m
function D = Genetic_Algorithm(train_features, train_targets, params, region);
% Classify using a basic genetic algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Para
www.eeworm.com/read/415311/11077320
m pocket.m
function [D, w_pocket] = Pocket(train_features, train_targets, alg_param, region)
% Classify using the pocket algorithm (an improvement on the perceptron)
% Inputs:
% features - Train features
www.eeworm.com/read/415311/11077324
m components_with_df.m
function D = Components_with_DF(train_features, train_targets, Ncomponents, region)
% Classify points using component classifiers with discriminant functions
% Inputs:
% train_features - Train f
www.eeworm.com/read/415311/11077327
m relaxation_ssm.m
function [D, a] = Relaxation_SSM(train_features, train_targets, params, region)
% Classify using the single-sample relaxation with margin algorithm
% Inputs:
% features - Train features
% targe
www.eeworm.com/read/415311/11077331
m gibbs.m
function D = Gibbs(train_features, train_targets, Ndiv, region)
% Classify using the Gibbs algorithm
% Inputs:
% features- Train features
% targets - Train targets
% Ndiv - Resolution of th
www.eeworm.com/read/410924/11264765
m backpropagation_batch.m
function [D, Wh, Wo] = Backpropagation_Batch(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train
www.eeworm.com/read/410924/11264766
m cascade_correlation.m
function D = Cascade_Correlation(train_features, train_targets, params, region)
% Classify using a backpropagation network with the cascade-correlation algorithm
% Inputs:
% features- Train feat
www.eeworm.com/read/410924/11264767
m nearest_neighbor.m
function D = Nearest_Neighbor(train_features, train_targets, Knn, region)
% Classify using the Nearest neighbor algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Knn