代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/222301/14697744
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a support vector classifier network using the specified tutor.
%
% load data/iris x y;
%
% C = 100;
% kernel = r
www.eeworm.com/read/120429/14803774
java collectionclassifier2.java
// Working collection classifier - Page 130
import java.util.*;
public class CollectionClassifier2 {
public static String classify(Collection c) {
return (c instanceof Set ? "Set"
www.eeworm.com/read/220289/14843806
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 prio
www.eeworm.com/read/217792/14951339
m classifierann.m
% ANN neural network classifier
function [mse,R2,accuracy] = classifierANN(data)
[nr, nc] = size(data)
nf = nc - 1; % number of features/attributes
% Transform the target represent
www.eeworm.com/read/213492/15133631
m quadclass.m
function [y,dfce]=quadclass( X, model)
% QUADCLASS Quadratic classifier.
%
% Synopsis:
% [y,dfce] = quadclass(X,model)
%
% Description:
% This function classifies input data X using quadratic
% dis
www.eeworm.com/read/213492/15133695
m~ rspoly2.m~
function red_model = redquadh(model)
% REDQUADH reduced SVM classifier with homogeneous quadratic kernel.
%
% Synopsis:
% red_model = redquadh(model)
%
% Description:
% It uses reduced set techique
www.eeworm.com/read/213492/15133737
m redquadh.m
function red_model = redquadh(model)
% REDQUADH reduced SVM classifier with homogeneous quadratic kernel.
%
% Synopsis:
% red_model = redquadh(model)
%
% Description:
% It uses reduced set techique
www.eeworm.com/read/213492/15133769
m tune_ocr.m
% TUNE_OCR Tunes SVM classifier for OCR problem.
%
% Description:
% The following steps are performed:
% - Training set is created from data in directory ExamplesDir.
% - Multi-class SVM is
www.eeworm.com/read/213240/15139965
m dlpdd.m
function W = dlpdd(x,nu,usematlab)
%DLPDD Distance Linear Programming Data Description
%
% W = DLPDD(D,NU)
%
% This one-class classifier works directly on the distance (dissimilarity)
% matrix
www.eeworm.com/read/212307/15160113
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 prio