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/150760/12265842
m~ rspoly2.m~
function red_model = redquadh(model)
% REDQUADH reduced SVM classifier with homogeneous quadratic kernel.
%
% Synopsis:
% red_model = redquadh(model)
%
% Description:
% It uses reduced set techique
www.eeworm.com/read/150760/12265983
m redquadh.m
function red_model = redquadh(model)
% REDQUADH reduced SVM classifier with homogeneous quadratic kernel.
%
% Synopsis:
% red_model = redquadh(model)
%
% Description:
% It uses reduced set techique
www.eeworm.com/read/150760/12266071
m tune_ocr.m
% TUNE_OCR Tunes SVM classifier for OCR problem.
%
% Description:
% The following steps are performed:
% - Training set is created from data in directory ExamplesDir.
% - Multi-class SVM is
www.eeworm.com/read/149739/12352748
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 (default: 10)
% S Standard deviation of weights in an input lay
www.eeworm.com/read/149739/12353703
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/130490/14190245
c identifier.c
/* Copyright (C) 2002 Mikael Ylikoski
* See the accompanying file "README" for the full copyright notice */
/**
* @file
* Language identifier.
* Uses N-gram classifier with N = 1..3.
*
* @autho
www.eeworm.com/read/128468/14295385
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/128193/14311415
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/223154/14651892
m ldbc.m
function [LDBC,ix]=ldbc(ECM,Y)
% Linear discriminant based classifier
% [LDBC] = ldbc(ECM);
% LDBC is a multiple discriminator
%
% [LD] = ldbc(ECM,D);
% calculates the LD to each class
%
% ECM
www.eeworm.com/read/223154/14651906
m mdbc.m
function [MDBC,ix]=mdbc(ECM,Y)
% Mahalanobis distance based classifier
% [MDBC] = mdbc(ECM);
% MDBC is a multiple discriminator
%
% [MD] = mdbc(ECM,D);
% calculates the MD to each class
% the m