代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/150761/12264624
m u_lindemo.m
echo off
%LINDEMO demonstration for using linear SVM classifier.
echo on;
clc
%LINDEMO demonstration for using linear SVM classifier.
%#########################################################
www.eeworm.com/read/150761/12264626
m c_clademo.m
echo off
% CLADEMO demonstration for using a contructed SVM classifier to classify
% input patterns
echo on;
%
%
% NOTICE: please first run any of the first three demonstrations before
%
www.eeworm.com/read/150761/12264656
m svmclass.m
function [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias, Parameters, nSV, nLabel)
% Usages:
% [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias);
% [Labels, DecisionValu
www.eeworm.com/read/150760/12264716
m contents.m
% Bayesian classification.
%
% bayescls - Bayesian classifier with reject option.
% bayesdf - Computes decision boundary of Bayesian classifier.
% bayeserr - Computes Bayesian risk for 1D case with G
www.eeworm.com/read/150760/12265705
m contents.m
% Visualization for pattern recognition.
%
% pandr - Visualizes solution of the Generalized Anderson's task.
% pboundary - Plots decision boundary of given classifier in 2D.
% pgauss
www.eeworm.com/read/150760/12265727
m pandr.m
function varargout = pandr(model,distrib)
% PANDR Visualizes solution of the Generalized Anderson's task.
%
% Synopsis:
% h = pandr(model)
%
% Description:
% It vizualizes solution of the Gen
www.eeworm.com/read/150760/12265752
m svmclass.m
function [y,dfce] = svmclass(X,model)
% SVMCLASS Support Vector Machines Classifier.
%
% Synopsis:
% [y,dfce] = svmclass( X, model )
%
% Description:
% [y,dfce] = svmclass( X, model ) classifies inp
www.eeworm.com/read/149739/12352700
m baggingc.m
%BAGGINGC Bootstrapping and aggregation of classifiers
%
% W = BAGGINGC (A,CLASSF,N,ACLASSF,T)
%
% INPUT
% A Training dataset.
% CLASSF The base classifier (default: nmc)
% N
www.eeworm.com/read/149739/12352736
m knn_map.m
%KNN_MAP Map a dataset on a K-NN classifier
%
% F = KNN_MAP(A,W)
%
% INPUT
% A Dataset
% W k-NN classifier trained by KNNC
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps t
www.eeworm.com/read/149739/12352739
m polyc.m
%POLYC Polynomial Classification
%
% W = polyc(A,CLASSF,N,S)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier (optional; default: FISHERC)
% N Degree of polynomial (optional;