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/235928/14041693
m knn.m
function [C,P]=knn(d, Cp, K)
%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix
%
% [C,P]=knn(d, Cp, [K])
%
% Input and output arguments ([]'s are optional):
% d (matrix)
www.eeworm.com/read/204766/15333851
m svmtrain.m
function net = svmtrain(net, X, Y, alpha0, dodisplay)
% SVMTRAIN - Train a Support Vector Machine classifier
%
% NET = SVMTRAIN(NET, X, Y)
% Train the SVM given by NET using the training data X wi
www.eeworm.com/read/386050/8767263
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/180305/9312998
m svm.m
function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
% SVM - Create a Support Vector Machine classifier
%
% NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)
%
www.eeworm.com/read/178917/9382296
m svm.m
function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
% SVM - Create a Support Vector Machine classifier
%
% NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)
%
www.eeworm.com/read/177674/9442540
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% per ro
www.eeworm.com/read/176823/9483223
m knnfwd.m
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
%
% Description
% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector
% per ro
www.eeworm.com/read/175683/9536333
m svm.m
function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
% SVM - Create a Support Vector Machine classifier
%
% NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)
%
www.eeworm.com/read/175683/9536348
asv svm.asv
function net = svm(nin, kernel, kernelpar, C, use2norm, qpsolver, qpsize)
% SVM - Create a Support Vector Machine classifier
%
% NET = SVM(NIN, KERNEL, KERNELPAR, C, USE2NORM, QPSOLVER, QPSIZE)
%
www.eeworm.com/read/357125/10215866
java mlknn.java
package mulan.classifier;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.TechnicalInformation;
import weka.core.Utils;
import weka.core.TechnicalInformation.Field;
imp