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/399158/7885696
m u_lindemo.m
echo off
%LINDEMO demonstration for using linear SVM classifier.
echo on;
clc
%LINDEMO demonstration for using linear SVM classifier.
%#########################################################
www.eeworm.com/read/399158/7885700
m c_clademo.m
echo off
% CLADEMO demonstration for using a contructed SVM classifier to classify
% input patterns
echo on;
%
%
% NOTICE: please first run any of the first three demonstrations before
%
www.eeworm.com/read/399158/7885739
m svmclass.m
function [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias, Parameters, nSV, nLabel)
% Usages:
% [Labels, DecisionValue]= SVMClass(Samples, AlphaY, SVs, Bias);
% [Labels, DecisionValu
www.eeworm.com/read/297340/8028999
tcl ns-node.tcl
# -*- Mode:tcl; tcl-indent-level:8; tab-width:8; indent-tabs-mode:t -*-
#
# * Modified and extended by Pablo Martin and Paula Ballester,
# * Strathclyde University, Glasgow.
# * June, 2003.
# *
#
# Co
www.eeworm.com/read/397111/8067105
m gauss_dd.m
%GAUSS_DD Gaussian data description.
%
% W = gauss_dd(A,fracrej,r)
%
% Fit a Gaussian density on dataset A. If requested, the r can be
% given to add some regularization to the estimated covar
www.eeworm.com/read/397111/8067213
m dd_ex3.m
% DD_EX3
%
% Show the use of the ksvdd: the support vector data description using
% several different kernels.
%
% To be honest, the SVDD is the most useful using the RBF kernel. In
% most case
www.eeworm.com/read/397111/8067299
m dd_roc_old.m
function [e,thr] = dd_roc(w,a,b,frac_rej)
% e = dd_roc(W,A,B,frac_rej)
%
% Find for a (data description) method W (trained with A) the
% Receiver Operating Characteristic curve over dataset B. The
www.eeworm.com/read/397111/8067331
m setthres.m
function out = setthres(w,thr)
%SETTHRES Set the threshold for a one-class classifier
%
% out = setthres(w,thr)
%
% The data of classifier w is copied to classifier out, only the
% threshold value
www.eeworm.com/read/397106/8067611
m contents.m
% Classification GUI and toolbox
% Version 1.0
%
% Modified by Vittorio Castelli, 2002 (vittorio@ee.columbia.edu)
%
% The topmost box in the user interface selects between
% "Original Framework" an
www.eeworm.com/read/397106/8067840
m classifierwrapper.m
% trains the classifier to distinguish between pairs of classes, computes
% majority vote, and labels accordingly.
% This is useful for classifiers such as Linear Discriminants, LS,
% perceptron etc.