代码搜索：Classifier

找到约 4,824 项符合「Classifier」的源代码

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java expsingle.java

/** * Single classifier solution. That is to say, we cluster all the instances * using the same clustering algorithms. * * * @author Waleed Kadous * @version $Id: ExpSingle.java,v 1.1.1.1 2
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www.eeworm.com/read/133885/5898954

java getpoints.java

/** * Single classifier solution. That is to say, we cluster all the instances * using the same clustering algorithms. * * * @author Waleed Kadous * @version $Id: GetPoints.java,v 1.1.1.1 2
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java~ tclass.java~

/** * Single classifier solution. That is to say, we cluster all the instances * using the same clustering algorithms. * * * @author Waleed Kadous * @version $Id: TClass.java,v 1.1.1.1 2002
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www.eeworm.com/read/128684/5980323

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
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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"
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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
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www.eeworm.com/read/493294/6399960

m rnnc.m

%RNNC Random Neural Net classifier % % W = RNNC(A,N,S) % % INPUT % A Input dataset % N Number of neurons in the hidden layer (default: 10) % S Standard deviation of weights in an input lay
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m setcost.m

%SETCOST Reset classification cost matrix of mapping % % W = SETCOST(W,COST,LABLIST) % % The classification cost matrix of the dataset W is reset to COST. % W has to be a trained classifier. CO
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www.eeworm.com/read/492400/6422222

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
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www.eeworm.com/read/489934/6463606

m demo.m

% % DEMONSTRATION OF ADABOOST_tr and ADABOOST_te % % Just type "demo" to run the demo. % % Using adaboost with linear threshold classifier % for a two class classification problem. % % Bug Reporting:
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