代码搜索：classifier

找到约 4,824 项符合「classifier」的源代码

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props explorer.props

# This props file contains default values for the Weka Explorer. # # Notes: # - backslashes within options, e.g., for the default "Classifier", need # to be doubled (the backslashes get interpreted
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www.eeworm.com/read/369609/2796579

props explorer.props

# This props file contains default values for the Weka Explorer. # # Notes: # - backslashes within options, e.g., for the default "Classifier", need # to be doubled (the backslashes get interpreted
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www.eeworm.com/read/293183/8310247

m polyc.m

%POLYC Polynomial Classification % % W = polyc(A,classf,n,s) % % Adds polynomial features to the dataset A and runs the untrained % classifier classf. n is the degree of the polynome (default 1).
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www.eeworm.com/read/293183/8310287

m kljlc.m

%KLJLC Linear classifier using KL expansion on the joint data. % % W = kljlc(A,n) % % Finds the linear discriminant function W for the dataset A % computing the ldc on a projection of the data on
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www.eeworm.com/read/415313/11076799

m optstumps.m

% OPTSTUMPS find a decision stump classifier to minimize % weighted empirical risk. % % [bestaxis, bestthresh, bestsign, wterr] = optstumps(patts,labels,wts) % bestaxis, bestthresh, bestsign: optima
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www.eeworm.com/read/431675/8661673

m invsigm.m

%INVSIGM Inverse sigmoid map % % W = W*invsigm % B = invsigm(A) % % Inverse sigmoidal transformation from classifier to map, transforming % posterior probabilities into distances. % % See also da
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www.eeworm.com/read/431675/8662114

m cleval.m

%CLEVAL Classifier evaluation (learning curve) % % [e,s] = cleval(classf,A,learnsizes,n,T,print) % % Generates at random for all class sizes of the training set % defined in the vector 'learnsizes
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www.eeworm.com/read/431675/8662117

m clevalb.m

%CLEVAL Classifier evaluation (learning curve), bootstrap version % % [e,s] = cleval(classf,A,learnsizes,n,T,print) % % Generates at random for all class sizes of the training set % defined in the
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www.eeworm.com/read/431675/8662119

m subsc.m

%SUBSC Subspace Classifier % % W = subsc(A,n) % % n-dimensional subspace maps are computed for each class of the dataset A % using PCA, such that they contain the origin. All object in A are normalize
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www.eeworm.com/read/386050/8768157

m nbayesc.m

%NBAYESC Bayes Classifier for given normal densities % % W = NBAYESC(U,G) % % INPUT % U Dataset of means of classes % G Covariance matrices (optional; default: identity matrices) % % OUTP
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