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m som_1d.m

function [W_out, p_out]=som_1d(P, nsofm, epochs, phas, W) % function W=som_1d(P, nsofm, lri, epochs, W) % % P - patterns % nsofm - number of neurons % epochs - number of epochs to trai
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m cl_fruits.m

% CL_fruits.m % Classify 4 different fruits into 4 bins % using a NN. % PenChen Chou, July, 17, 2001 % I/O Patterns In_pattern=[1 -1 -1;1 1 -1;-1 1 -1;1 -1 1]'; Target=[-1 -1;-1 1;1 -1;1 1
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makefile

# Makefile for Learning Bridge # # modification history # -------------------- # 01f,17jun05,tpw Use COMP_BRIDGE instead of hardcoded name-version # 01e,10jun05,tpw Refactor doc settings and move de
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htm learning-based computer vision with intel's open source computer vision library.htm

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h sspred_net2.h

#define IPERGRP (4) #define WINL (-7) #define WINR (7) #define NUM_IN ((WINR-WINL+1)*IPERGRP+3+1) /* number of input units */ #define NUM_HID (55) /* number of hidden units */ #define NUM_OUT (3)
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m cl_fruits.m

% CL_fruits.m % Classify 4 different fruits into 4 bins % using a NN. % PenChen Chou, July, 17, 2001 % I/O Patterns In_pattern=[1 -1 -1;1 1 -1;-1 1 -1;1 -1 1]'; Target=[-1 -1;-1 1;1 -1;1 1
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m pjh3.m

%LMS algorithm close all; clear all; N1=1000; %总点数 N2=512; %采样点数 K=100; %循环次数 w0=zeros(1,2); J_N2=0; sum=zeros(1,N2); %for learning curve uu=0.005; %param
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m som_1d.m

function [W_out, p_out]=som_1d(P, nsofm, epochs, phas, W) % function W=som_1d(P, nsofm, lri, epochs, W) % % P - patterns % nsofm - number of neurons % epochs - number of epochs to trai
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cpp asciiimport.cpp

// This is -*- C++ -*- // $Id: AsciiImport.cpp,v 1.8 1999/05/17 23:52:07 hp Exp $ /* * AsciiImport.cpp * * Copyright (C) 1998 Asger Alstrup Nielsen * * Developed by Asger Alstrup Nielsen
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h optimize.h

// -*- C++ -*- #ifndef __LEMGA_OPTIMIZE_H__ #define __LEMGA_OPTIMIZE_H__ /** @file * @brief Some generic optimization algorithms. * * $Id: optimize.h 1917 2005-01-07 01:25:30Z ling $ */ #inclu
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