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/191902/8417415
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/191902/8417419
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/286662/8751637
m ml_diag.m
function test_targets = ML_diag(train_patterns, train_targets, test_patterns, AlgorithmParameters)
% Classify using the maximum likelyhood algorithm with diagonal covariance matrices
% Inputs:
%
www.eeworm.com/read/286662/8752023
m components_with_df.m
function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify points using component classifiers with discriminant functions
% Inputs:
www.eeworm.com/read/177129/9468750
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/177129/9468752
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/177129/9468754
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
www.eeworm.com/read/177129/9468770
m bayesian_model_comparison.m
function D = Bayesian_Model_Comparison(train_features, train_targets, Ngaussians, region)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
% the maximum nu
www.eeworm.com/read/177129/9468804
m interactive_learning.m
function D = Interactive_Learning(train_features, train_targets, params, region);
% Classify using nearest neighbors and interactive learning
% Inputs:
% features- Train features
% targets - Tr
www.eeworm.com/read/177129/9468808
m svm.m
function [D, a_star] = SVM(train_features, train_targets, params, region)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
% features- Train