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/493206/6398591
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/493206/6398606
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/493206/6398607
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/493206/6398608
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/493206/6398609
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/482389/6623868
info talks.info
A simple text classification problem: classify postings to /usr/msgs
as talk announcements or "other". There are 818 messages, going back
to Aug 93. I used messages numbered 500 and up as test cases
www.eeworm.com/read/410924/11264784
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/410924/11264814
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/410924/11264818
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/410924/11264842
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