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/357991/3009661
java multilayerperceptronoperatorresult.java
package eti.bi.alphaminer.patch.standard.operation.result;
import java.util.ArrayList;
import javax.swing.JTable;
import javax.swing.table.TableColumnModel;
import weka.classifiers.Classifier;
www.eeworm.com/read/261925/4319274
py mboxtest.py
#! /usr/bin/env python
"""mboxtest.py: A test driver for classifier.
Usage: mboxtest.py [options]
Options:
-f FMT
One of unix, mmdf, mh, or qmail. Specifies mailbox format
www.eeworm.com/read/294611/8216787
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/293183/8310195
m minc.m
%MINC Minimum combining classifier
%
% W = minc(V)
% W = V*minc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the minimum combiner: it selects the cla
www.eeworm.com/read/293183/8310602
m meanc.m
%MEANC Averaging combining classifier
%
% W = meanc(V)
% W = V*meanc
%
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the mean combiner: it selects the c
www.eeworm.com/read/293183/8310686
m majorc.m
%MAJORC Majority combining classifier
%
% W = majorc(V)
% W = v*majorc
%
% If V = [V1,V2,V3,...] is a stacked set of classifiers trained for
% the same classes and W is the majority combiner: it se
www.eeworm.com/read/367875/9725038
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/367655/9738564
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/367442/9748106
m oaoclass.m
function [labels,votes] = oaoclass(data,model)
% OAOCLASS One-Against-One SVM classifier.
% [labels,votes] = oaoclass(data,model)
%
% Inputs:
% data [dim x num_data] data to be classified.
% Model [
www.eeworm.com/read/248950/12534043
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