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/386050/8768976
m testc.m
%TESTC Test classifier, error / performance estimation
%
% [E,C] = TESTC(A*W,TYPE)
% [E,C] = TESTC(A,W,TYPE)
% E = A*W*TESTC([],TYPE)
%
% [E,F] = TESTC(A*W,TYPE,LABEL)
% [E,F] = TESTC(A,
www.eeworm.com/read/386050/8769080
m mapping.m
%MAPPING Mapping class constructor
%
% W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, SIZE_IN, SIZE_OUT)
%
% A map/classifier object is constructed. It may be used to map a dataset A
% on anoth
www.eeworm.com/read/428849/8834612
m contents.m
% Support Vector Machines.
%
% bsvm2 - Solver for multi-class BSVM with L2-soft margin.
% evalsvm - Trains and evaluates Support Vector Machines classifier.
% mvsvmclass - Majority votin
www.eeworm.com/read/183443/9158846
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/183443/9158976
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/183443/9158984
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/379774/9177371
cpp leftview.cpp
// LeftView.cpp : implementation of the CLeftView class
//
#include "stdafx.h"
#include "svmcls.h"
#include "svmclsDoc.h"
#include "LeftView.h"
#include "svmclsView.h"
#include "classifier.
www.eeworm.com/read/181389/9256469
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/181389/9256561
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/181389/9256567
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%