代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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m mapping.m
%MAPPING Mapping class constructor
%
% W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, SIZE_IN, SIZE_OUT)
%
% A map/classifier object is constructed. It may be used to map a dataset A
% on anoth
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java naivebayes.java
package ir.classifiers;
import java.io.*;
import java.util.*;
import ir.vsr.*;
import ir.utilities.*;
/**
* Implements the NaiveBayes Classifier with Laplace smoothing. Stores probabilities
* inte
www.eeworm.com/read/450608/7480388
m lmnc.m
%LMNC Levenberg-Marquardt trained feed-forward neural net classifier
%
% [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each
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m mapping.m
%MAPPING Mapping class constructor
%
% W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, SIZE_IN, SIZE_OUT)
%
% A map/classifier object is constructed. It may be used to map a dataset A
% on anoth
www.eeworm.com/read/450549/7481636
cpp classifierdlg.cpp
// ClassifierDlg.cpp : implementation file
//
#include "stdafx.h"
#include "Classifier.h"
#include "ClassifierDlg.h"
#include "svm.h"
#include
using namespace std;
#ifdef _DEBUG
#def
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m lmnc.m
%LMNC Levenberg-Marquardt trained feed-forward neural net classifier
%
% [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each hid
www.eeworm.com/read/441245/7673241
m testc.m
%TESTC Test classifier, error / performance estimation
%
% [E,C] = TESTC(A*W,TYPE)
% [E,C] = TESTC(A,W,TYPE)
% E = A*W*TESTC([],TYPE)
%
% [E,F] = TESTC(A*W,TYPE,LABEL)
% [E,F] = TESTC(A,
www.eeworm.com/read/441245/7673266
m mapping.m
%MAPPING Mapping class constructor
%
% W = MAPPING(MAPPING_FILE, MAPPING_TYPE, DATA, LABELS, SIZE_IN, SIZE_OUT)
%
% A map/classifier object is constructed. It may be used to map a dataset A
% on anoth
www.eeworm.com/read/299459/7850374
m contents.m
% Support Vector Machines.
%
% bsvm2 - Solver for multi-class BSVM with L2-soft margin.
% evalsvm - Trains and evaluates Support Vector Machines classifier.
% mvsvmclass - Majority votin
www.eeworm.com/read/398324/7994150
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%