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/493206/6398458
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/493206/6398459
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/493206/6398469
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/493206/6398487
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/493206/6398489
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
www.eeworm.com/read/493206/6398495
m perceptron_fm.m
function [D, a] = Perceptron_FM(train_features, train_targets, params, region)
% Classify using the Perceptron algorithm but at each iteration updating the worst-classified sample
% Inputs:
% fe
www.eeworm.com/read/493206/6398500
m ada_boost.m
function D = ada_boost(train_features, train_targets, params, region);
% Classify using the AdaBoost algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Params - [Numbe
www.eeworm.com/read/493206/6398551
m components_without_df.m
function D = Components_without_DF(train_features, train_targets, Classifiers, region)
% Classify points using component classifiers with discriminant functions
% Inputs:
% train_features - Trai
www.eeworm.com/read/493206/6398575
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/493206/6398583
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