代码搜索：classification
找到约 3,679 项符合「classification」的源代码
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www.eeworm.com/read/292984/3935758
m hmemenu.m
% dataset -> (1=>user data) or (2=>toy example)
% type -> (1=> Regression model) or (2=>Classification model)
% num_glevel -> number of hidden nodes in the net (gating levels)
% num_
www.eeworm.com/read/292964/3936906
m hmemenu.m
% dataset -> (1=>user data) or (2=>toy example)
% type -> (1=> Regression model) or (2=>Classification model)
% num_glevel -> number of hidden nodes in the net (gating levels)
% num_
www.eeworm.com/read/443386/1750051
c ipt_classify.c
/*
* This is a module which is used for setting the skb->priority field
* of an skb for qdisc classification.
*/
/* (C) 2001-2002 Patrick McHardy
*
* This program is free softw
www.eeworm.com/read/434858/1867948
m hmemenu.m
% dataset -> (1=>user data) or (2=>toy example)
% type -> (1=> Regression model) or (2=>Classification model)
% num_glevel -> number of hidden nodes in the net (gating levels)
% num_
www.eeworm.com/read/428780/1954255
m contents.m
% Generalized Anderson's task.
%
% andrerr - Classification error of the Generalized Anderson's task.
% androrig - Original method to solve the Anderson's task.
% eanders - Epsilon-solutio
www.eeworm.com/read/411379/2188994
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/411379/2189012
m dagsvm.m
function net = dagsvm(arg)
% PAIRWISE
%
% Construct a dag-svm multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class dagsvm network!)
%
%
www.eeworm.com/read/396844/2406726
m confmat.m
function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the prediction
www.eeworm.com/read/396844/2406736
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of which
www.eeworm.com/read/393286/2485513
c ipt_classify.c
/*
* This is a module which is used for setting the skb->priority field
* of an skb for qdisc classification.
*/
/* (C) 2001-2002 Patrick McHardy
*
* This program is free softw