代码搜索：Classifier

找到约 4,824 项符合「Classifier」的源代码

代码结果 4,824
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m fisherp.m

function [alphas,solution,t]=fisherp(X,J,K,tmax,t,alphas) % FISHERP learns the Fisher classifier using Perceptron rule. % [alphas,solution,t]=fisherk(X,J,K,tmax,t,alphas) % % FISHERP This algorithm f
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www.eeworm.com/read/266483/4272284

m fisherk.m

function [alphas,solution,t]=fisherk(X,J,K,tmax,t,alphas) % FISHERK learns the Fisher classifier using Kozinec's rule. % [alphas,solution,t]=fisherk(X,J,K,tmax,t,alphas) % % FISHERK This algorith
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www.eeworm.com/read/266483/4272286

m fisherp.m

function [alphas,solution,t]=fisherp(X,J,K,tmax,t,alphas) % FISHERP learns the Fisher classifier using Perceptron rule. % [alphas,solution,t]=fisherk(X,J,K,tmax,t,alphas) % % FISHERP This algorithm f
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www.eeworm.com/read/293183/8310239

m knn_map.m

%KNN_MAP Map a dataset on a K-NN based classifier % % F = knn_map(A,W) % % Maps the dataset A by the K-NN classfier W on the [0,1] interval % for each of the classes W is trained on. The posterior
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www.eeworm.com/read/367442/9747945

m oaaclass.m

function [labels,dfce,multi_dfce] = oaaclass(data,model) % OAACLASS One-Against-All SVM classifier. % [labels] = oaaclass(data,model) % % Inputs: % data [dim x num_data] data to be classified. % Mo
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www.eeworm.com/read/367442/9748270

m fisherk.m

function [alphas,solution,t]=fisherk(X,J,K,tmax,t,alphas) % FISHERK learns the Fisher classifier using Kozinec's rule. % [alphas,solution,t]=fisherk(X,J,K,tmax,t,alphas) % % FISHERK This algorith
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www.eeworm.com/read/367442/9748276

m fisherp.m

function [alphas,solution,t]=fisherp(X,J,K,tmax,t,alphas) % FISHERP learns the Fisher classifier using Perceptron rule. % [alphas,solution,t]=fisherk(X,J,K,tmax,t,alphas) % % FISHERP This algorithm f
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www.eeworm.com/read/411674/11233723

m evalsvm.m

function [best_model,Errors] = evalsvm(arg1,arg2,arg3) % EVALSVM Trains and evaluates Support Vector Machines classifier. % % Synopsis: % [model,Errors] = evalsvm(data,options) % [model,Errors] = ev
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www.eeworm.com/read/204456/15339245

m lpdd.m

function W = lpdd(x,nu,s,dtype,par) %LPDD Linear programming distance data description % % W = LPDD(X,NU,S,DTYPE,P) % % One-class classifier put into a linear programming framework. From % th
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www.eeworm.com/read/431675/8661706

m persc.m

%PERSC Linear classifier by non-linear perceptron % % [W1,W2] = persc(A,n,step,target,W) % % Finds the linear discriminant function W1 (a mapping) by n cycles % of the data through the non-linear
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