代码搜索：sequential
找到约 1,846 项符合「sequential」的源代码
代码结果 1,846
www.eeworm.com/read/140850/13059610
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/240686/13204827
h dsearch.h
#ifndef DATATYPE_SEARCH_METHODS
#define DATATYPE_SEARCH_METHODS
// search the n element arrray a for a match with key
// using the sequential search. return the index of the
// matching array el
www.eeworm.com/read/314717/13560465
ps viewjpeg.ps
%! viewjpeg.ps Copyright (C) Thomas Merz 1994
%
% View JPEG files with Ghostscript
%
% This PostScript code relies on level 2 features.
%
% Only JPEG baseline, extended sequential, and progres
www.eeworm.com/read/312163/13617421
c smo_mex.c
/* --------------------------------------------------------------------
smo_mex.c: MEX-file for Sequential Minimal Optimizer.
Compile: mex smo_mex.c ../kernels/kernel_fun.c
Synopsis:
[Alpha,b
www.eeworm.com/read/134901/5891499
c smo_mex.c
/* --------------------------------------------------------------------
smo_mex.c: MEX-file for Sequential Minimal Optimizer.
Compile: mex smo_mex.c ../kernels/kernel_fun.c
Synopsis:
[Alpha,b
www.eeworm.com/read/133837/5899246
c seq.c
/* seq.c - This is the sequential visit of the database. This defines two
user-visable routines that are used together. This is the DBM interface. */
/* This file is part of GDBM, the GNU data b
www.eeworm.com/read/128684/5980357
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/486842/6530685
c svm_smo.c
/* Copyright (C) 1999 Greg Schohn - gcs@jprc.com */
/* ********************* svm_smo.c **********************
* John Platt's Sequential Minimal Optimization algorithm
* (http://www.research.micros
www.eeworm.com/read/483114/6609689
asv train.asv
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/483114/6609693
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut