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/170936/9779281
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/415313/11076541
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/413912/11137244
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/248950/12531338
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/204766/15333837
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/431675/8661762
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
www.eeworm.com/read/286490/8762815
java id3.java
package weka.classifiers.trees;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Instance;
import w
www.eeworm.com/read/386050/8767371
m qdc.m
%QDC Quadratic Bayes Normal Classifier (Bayes-Normal-2)
%
% [W,R,S,M] = QDC(A,R,S,M)
% W = A*QDC([],R,S)
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/428849/8834615
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
www.eeworm.com/read/175317/9552364
m tsvm.m
function classifier=tsvm(X,Y,Kernel,KernelParam,lambda)
% TSVM Implements Transductive SVMs
% [alpha,b]=tsvm(X,Y,Kernel,KernelParam,C)
% C = 1/(2*l*lambda)
%
% Inputs:
% X : (num x dim) examples ar