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/149739/12353495
m lmnc.m
%LMNC Levenberg-Marquardt trained feed-forward neural net classifier
%
% [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each
www.eeworm.com/read/149739/12353647
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/130491/14189818
1 dbacl.1
\" t
.TH DBACL 1 "Bayesian Text Classification Tools" "Version 1.3" ""
.SH NAME
dbacl \- a digramic Bayesian classifier for text recognition.
.SH SYNOPSIS
.HP
.B dbacl
[-dvnirMND]
[-T
.IR type
] -l
www.eeworm.com/read/128468/14295332
m knnclass.m
function [class,index,dist] = knnclass(tst,X,I,K)
% [class,index,dist] = knnclass(tst,X,I,K)
%
% KNNCLASS is an implementation of K-Nearest Neighbours
% classifier. The Euclidean metric is used.
%
%
www.eeworm.com/read/128193/14311433
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/128193/14311519
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/128193/14311525
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/222301/14697761
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/222301/14697839
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/222301/14697844
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.
%