代码搜索：Learning
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www.eeworm.com/read/458392/7297231
m knn.m
function [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% knn implementation
%
% USE : [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% xapp, yapp : learning data
% valY : all the Y value pos
www.eeworm.com/read/451308/7467554
java cvlearningcurve.java
package ir.classifiers;
import java.io.*;
import java.util.*;
import ir.vsr.*;
import ir.utilities.*;
/**
* Gives learning curves with K-fold cross validation for a classifier.
*
* @author
www.eeworm.com/read/298871/7928700
m nnd12vl.m
function nnd12vl(cmd,arg1)
%NND12VL Variable learning rate backpropagation demonstration.
%
% This demonstration requires the Neural Network Toolbox.
% First Version, 8-31-95.
%==============
www.eeworm.com/read/398337/7993680
m knn.m
function [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% knn implementation
%
% USE : [ypred,tabkppv,distance]=knn(xapp,yapp,valY,X,k)
%
% xapp, yapp : learning data
% valY : all the Y value pos
www.eeworm.com/read/397102/8068556
m learnlm.m
function j = learnlm(p,d)
%LEARNLM Levenberg-Marquardt learning rule.
%
% LEARNLM(P,D)
% P - RxQ matrix of input (column) vectors.
% D - SxQ matrix of delta (column) vectors.
% Returns:
% Par
www.eeworm.com/read/143498/12870304
m kappa.m
function new_kappa = adjkappa(kappa, rmse)
% ADJKAPPA Adjust learning rate eta in SD according to history of RMSE.
inc_rate = 1.1;
dec_rate = 0.9;
leng = length(rmse);
if leng < 5,
new
www.eeworm.com/read/321263/13409877
m programs_17a.m
% Chapter 17 - Neural Networks.
% Programs_17a - The generalized delta learning rule (Figure 17.7).
% Copyright Birkhauser 2004. Stephen Lynch.
function Programs_17a
% Load Boston housing data.
load
www.eeworm.com/read/133885/5898911
java expseg.java
/**
* Naive segmentation-based learning. Used for comparison.
*
* @author Waleed Kadous
* @version $Id: ExpSeg.java,v 1.1.1.1 2002/06/28 07:36:16 waleed Exp $
*/
package tclass;
import tcla
www.eeworm.com/read/130196/5963090
m init_learn.m
function [bb, Protocol]=init_learn(bb, dataset)
% [bb, Protocol]=init_learn(bb, dataset)
%
% performs some initializations before learning
% G. Raetsch 10.12.99
% Copyright (c) 1998,1999 GMD Ber