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/431675/8662250
m classim.m
%CLASSIM Classify image using a given classifier
%
% labels = classim(D,N)
%
% Returns an image with the labels of the classified datasetimage D
% (typically the result of a mapping or classificat
www.eeworm.com/read/429657/8795795
cs category.cs
using System;
///
/// Class Item
/// Represents a category of Items the small business uses to
///classify/organize its Items
///
public class Category
{
private
www.eeworm.com/read/418695/10935576
m classim.m
%CLASSIM Classify image using a given classifier
%
% labels = classim(D,N)
%
% Returns an image with the labels of the classified datasetimage D
% (typically the result of a mapping or classificat
www.eeworm.com/read/461381/7228404
m classif.m
function classification = classif(Ytrain, Ytest)
% classification = classify(Ytrain, Ytest)
%
% Given the train matrix Ytrain and the test matrix Ytest,
% this function returs a vector classificat
www.eeworm.com/read/455967/7360555
m knnclassification.m
function result = knnclassification(testsamplesX,samplesX, samplesY, Knn,type)
% Classify using the Nearest neighbor algorithm
% Inputs:
% samplesX - Train samples
% samplesY - Train labe
www.eeworm.com/read/397102/8068464
m classim.m
%CLASSIM Classify image using a given classifier
%
% labels = classim(D,N)
%
% Returns an image with the labels of the classified datasetimage D
% (typically the result of a mapping or classificat
www.eeworm.com/read/286592/6282701
m knnclassification.m
function result = knnclassification(testsamplesX,samplesX, samplesY, Knn,type)
% Classify using the Nearest neighbor algorithm
% Inputs:
% samplesX - Train samples
% samplesY - Train labe
www.eeworm.com/read/490049/6457794
m knnclassification.m
function result = knnclassification(testsamplesX,samplesX, samplesY, Knn,type)
% Classify using the Nearest neighbor algorithm
% Inputs:
% samplesX - Train samples
% samplesY - Train labe
www.eeworm.com/read/344640/11870086
m svmclass.m
function [Labels, DecisionValue]= SVMClass(Samples,AlphaY, SVs, Bias, Parameters)
% USAGE:
% [Labels, DecisionValue]= SVMClass(Samples,AlphaY, SVs, Bias, Parameters)
%
% DESCRIPTION:
% Classify
www.eeworm.com/read/342008/12047431
m classim.m
%CLASSIM Classify image using a given classifier
%
% labels = classim(D,N)
%
% Returns an image with the labels of the classified datasetimage D
% (typically the result of a mapping or classificat