代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/411674/11233871
m ocr_fun.m
function ocr_fun(data)
% OCR_FUN Calls OCR classifier and displays result.
%
% Synopsis:
% ocr_fun(data)
%
% Description:
% This function classifies images of characters stored as columns
% of th
www.eeworm.com/read/248950/12531367
m svmfwd.m
function [Y, Y1] = svmfwd(net, X)
% SVMFWD - Forward propagation through Support Vector Machine classifier
%
% Y = SVMFWD(NET, X)
% For a data structure NET, the matrix of vectors X is input into
www.eeworm.com/read/204766/15333845
m svmfwd.m
function [Y, Y1] = svmfwd(net, X)
% SVMFWD - Forward propagation through Support Vector Machine classifier
%
% Y = SVMFWD(NET, X)
% For a data structure NET, the matrix of vectors X is input into
www.eeworm.com/read/201218/15413235
m deltablssvm.m
function model = deltablssvm(model,a1,a2)
% Bias term correction for the LS-SVM classifier
%
% >> model = deltablssvm(model, b_new)
%
% This function is only useful in the object oriented function
%
www.eeworm.com/read/188386/8544465
h bfagent.h
// Interface for BFagent -- Classifier predictors
#import "Agent.h"
#import "BFParams.h"
#import "BFCast.h"
#import
#import "World.h"
//pj: // Structure for list of indiv
www.eeworm.com/read/289334/8558640
m knnclass.m
function y = knnclass(X,model)
% KNNCLASS k-Nearest Neighbours classifier.
%
% Synopsis:
% y = knnclass(X,model)
%
% Description:
% The input feature vectors X are classified using the K-NN
% rule
www.eeworm.com/read/431675/8661871
m parsc.m
%PARSC Pars classifier
%
% parsc(w)
%
% Displays the type and, for combining classifiers, the structure of
% the mapping w.
%
% See also mappings
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
www.eeworm.com/read/431675/8662087
m roc.m
%ROC Receiver-operator curve
%
% e = roc(D,k)
%
% Computes k points of the receiver-operator curve of the classifier
% W for the labeled data set D, which is typically the result of
% D = A*W*clas
www.eeworm.com/read/431675/8662175
m reject.m
%REJECT Compute error-reject trade-off curve
%
% e = reject(D)
%
% Computes the error-reject curve of the classification result
% D = A*W, in which A is a dataset and W a classifier. e is a
% set
www.eeworm.com/read/386050/8767469
m svc.m
%SVC Support Vector Classifier
%
% [W,J] = SVC(A,KERNEL,C)
% [W,J] = SVC(A,TYPE,PAR,C)
% W = A*SVC([],KERNEL,C)
% W = A*SVC([],TYPE,PAR,C)
%
% INPUT
% A Dataset
% KERNEL - Un