代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

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m baggingc.m

%BAGGINGC Bootstrapping and aggregation of classifiers % % W = baggingc(A,classf,n,cclassf,T) % % Computation of a stabilized version of a classifier by % bootstrapping and aggregation ('bagging
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m rbnc.m

%RBNC Radial basis neural net classifier % % W = rbnc(A,n) % % A feedforward neural network classifier with one hidden layer with % at most n radial basis units is computed for the labeled dataset
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m knnc.m

%KNNC K-Nearest Neighbor Classifier % % [W,k,e] = knnc(A,k) % % Computation of the k-nearest neigbor classifier for the dataset A. % Default k: optimize leave-one-out error e. W is a mapping and %
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m parzenc.m

%PARZENC Optimisation of the Parzen classifier % % [W,h,e] = parzenc(A) % % Computation of the optimum smoothing parameter h for the Parzen % classifier between the classes in the dataset A. The l
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m rsubc.m

%RSUBC Random Subspace Classifier % % W = rsubc(A,classf,r,n,cclassf,T) % % Computation of a combined classifier by selecting n random subsets % of r features. For each of these subsets the base c
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m getclass.m

%GETCLASS Get classifier bit of mapping function classbit = getclass(w) classbit = w.s; return
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m isclassifier.m

%ISCLASSIFIER Get classifier bit of mapping function classbit = isclassifier(w) classbit = w.s; return
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m mclassc.m

%MCLASSC Computation of multi-class classifier from 2-class discriminants % % W = mclassc(A,classf) % % The untrained classifier classf is called to compute c classifiers % between each of the c class
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m loglc.m

%LOGLC Logistic Linear Classifier % % W = LOGLC(A) % % INPUT % A Dataset % % OUTPUT % W Logistic linear classifier % % DESCRIPTION % Computation of the linear classifier for the dataset
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m baggingc.m

%BAGGINGC Bootstrapping and aggregation of classifiers % % W = BAGGINGC (A,CLASSF,N,ACLASSF,T) % % INPUT % A Training dataset. % CLASSF The base classifier (default: nmc) % N
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