代码搜索：classifier
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www.eeworm.com/read/417741/10977079
java bayesresult.java
package ir.classifiers;
import java.util.*;
/**
* An object to hold the result of training a NaiveBayes classifier.
* Stores the class priors and the counts of features in each class.
*
* @autho
www.eeworm.com/read/469416/6976407
m demknn1.m
%DEMKNN1 Demonstrate nearest neighbour classifier.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with
www.eeworm.com/read/299984/7139922
m spatm.m
%SPATM Augment image dataset with spatial label information
%
% E = SPATM(D,S)
% E = D*SPATM([],S)
%
% INPUT
% D image dataset classified by a classifier
% S smoothing paramet
www.eeworm.com/read/299984/7139984
m averagec.m
%AVERAGEC Combining of linear classifiers by averaging coefficients
%
% W = AVERAGEC(V)
% W = V*AVERAGEC
%
% INPUT
% V A set of affine base classifiers.
%
% OUTPUT
% W Combined classifier.
%
%
www.eeworm.com/read/299984/7139987
m rbnc.m
%RBNC Radial basis function neural network classifier
%
% W = RBNC(A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of RBF units in hidden layer
%
% OUTPUT
% W Radial basis neural n
www.eeworm.com/read/299984/7140530
m costm.m
%COSTM Cost mapping, classification using costs
%
% Y = COSTM(X,C,LABLIST)
% W = COSTM([],C,LABLIST)
%
% DESCRIPTION
% Maps the classifier output X (assumed to be posterior probability
% estimate
www.eeworm.com/read/299984/7140770
m prex_plotc.m
%PREX_PLOTC PRTools example on the dataset scatter and classifier plot
help prex_plotc
n = prprogress;
prprogress off
echo on
% Generate Higleyman data
A = gendath([100 100]);
www.eeworm.com/read/460435/7250397
m spatm.m
%SPATM Augment image dataset with spatial label information
%
% E = SPATM(D,S)
% E = D*SPATM([],S)
%
% INPUT
% D image dataset classified by a classifier
% S smoothing paramet
www.eeworm.com/read/460435/7250459
m averagec.m
%AVERAGEC Combining of linear classifiers by averaging coefficients
%
% W = AVERAGEC(V)
% W = V*AVERAGEC
%
% INPUT
% V A set of affine base classifiers.
%
% OUTPUT
% W Combined classifier.
%
%