代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/351797/10609647
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a support vector classifier network using the specified tutor.
%
% load data/iris x y;
%
% C = 100;
% kernel = r
www.eeworm.com/read/421949/10676546
m bayescln.m
function [I,Pkx]=bayescln(X,MI,SIGMA,Pk)
% BAYESCLN Bayes classifier for Gaussian distributiuon.
% [I,Pkx]=bayescln(X,MI,SIGMA,Pk)
%
% This function classifies into the class according to the
%
www.eeworm.com/read/421949/10676564
m~ bayescln.m~
function [I,Pkx]=bayescln(X,MI,SIGMA,Pk)
% BAYESCLN Bayes classifier for Gaussian distributiuon.
% [I,Pkx]=bayescln(X,MI,SIGMA,Pk)
%
% This function classifies into the class according to the
%
www.eeworm.com/read/418755/10928180
m demo.m
%
% DEMONSTRATION OF ADABOOST_tr and ADABOOST_te
%
% Just type "demo" to run the demo.
%
% Using adaboost with linear threshold classifier
% for a two class classification problem.
%
% Bug Reporting:
www.eeworm.com/read/418695/10935744
m testd.m
%TESTD Classification error estimate
%
% [e,j,k,l] = testd(A,W,r,iter)
%
% Test of dataset A on the classifier defined by W. Returns:
% e - the fraction of A that is incorrectly classified by W.
%
www.eeworm.com/read/467949/6997137
m demo.m
%
% DEMONSTRATION OF ADABOOST_tr and ADABOOST_te
%
% Just type "demo" to run the demo.
%
% Using adaboost with linear threshold classifier
% for a two class classification problem.
%
% Bug Reporting:
www.eeworm.com/read/466142/7039596
m do_naive_bayes.m
function do_naive_bayes(config_file)
%% Function that runs the Naive Bayes classifier on histograms of
%% vector-quantized image regions. Based on the paper:
%%
%% Visual categorization with ba
www.eeworm.com/read/299984/7140004
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer
% S Standard deviation of weights in an input layer (default: 1
www.eeworm.com/read/299984/7140350
m mogc.m
%MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
%
% INPUT
% A Dataset
% N Number of mixtures (optional; default 2)
% R,S Regularization parameters, 0
www.eeworm.com/read/299984/7140542
m lssvc.m
function W = lssvc(A, TYPE, PAR, C)
%LSSVC Least-Squares Support Vector Classifier
%
% W = lssvc(A,TYPE,PAR,C);
%
% INPUT
% A dataset
% TYPE Type of the kernel (optional; default: '