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/436088/7777278
cs testresultsform.cs
using System;
using System.Windows.Forms;
// To use the classifier performance
using FinanceAI.AI;
namespace FinanceAI.UI
{
public partial class TestResultsForm : Form
{
/
www.eeworm.com/read/299459/7850706
m train_ocr.m
% TRAIN_OCR Training of OCR classifier based on multiclass SVM.
%
% Description:
% The following steps are performed:
% - Training set is created from data in directory ExamplesDir.
% - Mult
www.eeworm.com/read/397106/8067522
m em_vc.m
% Learns classifier and classifies test set
% using the expectation-maximization algorithm
% Uses a modified version of E-M which automatically selects the number of components
%
% Usage:
% [trai
www.eeworm.com/read/397106/8067574
m k_l_nn_rule_vc.m
% Classifies input using (k-l)NN classifier
% This means that it will classify the input if at least l of the k nearest
% neighbors agree on the label, and refuses to classify otherwise.
%
% NOTE: To
www.eeworm.com/read/397106/8067879
m svm_vc.m
% Learns classifier and classifies test set
% using Support Vector Machines.
% The actual SVM Code is GNU code (see SVMReadme.txt)
%
%
% If there are 2 classes, there is no problem (except that the
www.eeworm.com/read/397102/8067967
m invsigm.m
%INVSIGM Inverse sigmoid map
%
% W = W*invsigm
% B = invsigm(A)
%
% Inverse sigmoidal transformation from classifier to map, transforming
% posterior probabilities into distances.
%
% See also da
www.eeworm.com/read/397102/8068279
m cleval.m
%CLEVAL Classifier evaluation (learning curve)
%
% [e,s] = cleval(classf,A,learnsizes,n,T,print)
%
% Generates at random for all class sizes of the training set
% defined in the vector 'learnsizes
www.eeworm.com/read/397102/8068283
m clevalb.m
%CLEVAL Classifier evaluation (learning curve), bootstrap version
%
% [e,s] = cleval(classf,A,learnsizes,n,T,print)
%
% Generates at random for all class sizes of the training set
% defined in the
www.eeworm.com/read/397102/8068287
m subsc.m
%SUBSC Subspace Classifier
%
% W = subsc(A,n)
%
% n-dimensional subspace maps are computed for each class of the dataset A
% using PCA, such that they contain the origin. All object in A are normalize
www.eeworm.com/read/328078/13047126
m plotdr.m
function plotdr(f, varargin)
%PLOTDR Plot decision regions for classifier object.
% PLOTDR(F, ...) plots the decision boundaries of maximum posterior
% likelihood for different classes where F is