代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
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www.eeworm.com/read/316604/13520395
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The
www.eeworm.com/read/133885/5898881
java classifieri.java
package tclass;
/**
* The interface for a classifier. These objects are produced by
* learners.
*
*
* @author Waleed Kadous
* @version $Id: ClassifierI.java,v 1.1.1.1 2002/06/28 07
www.eeworm.com/read/122504/6065343
java classifier.java
/*
* Classifier.java
*
* Created on March 28, 2001, 1:49 PM
*/
package uk.ac.leeds.ccg.geotools.classification;
import com.sun.java.util.collections.List;
/** Defines the Classifier i
www.eeworm.com/read/359185/6352484
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The
www.eeworm.com/read/493206/6398462
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The
www.eeworm.com/read/410924/11264772
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The
www.eeworm.com/read/405069/11472166
m classification_error.m
function [classify, err] = classification_error(D, patterns, targets, region)
%Find a classification error for a given decision surface D and a given set of
%patterns (2xL) and targets (1xL)
%The
www.eeworm.com/read/259886/11759490
m demosm2.m
%% A Two-dimensional Self-organizing Map
% As in DEMOSM1, this self-organizing map will learn to represent different
% regions of the input space where input vectors occur. In this demo, however,
www.eeworm.com/read/259886/11759585
m demop5.m
%% Normalized Perceptron Rule
% A 2-input hard limit neuron is trained to classify 5 input vectors into two
% categories. Despite the fact that one input vector is much bigger than the
% others, t
www.eeworm.com/read/131588/14136158
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The