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Posted 5 Dec 2006

Neural Network for Recognition of Handwritten Digits

, 5 Dec 2006
A convolutional neural network achieves 99.26% accuracy on a modified NIST database of hand-written digits.
#if !defined(AFX_DLGBACKPROPPARAMETERS_H__58993721_5E08_4AB0_85D1_D00AE597CCC7__INCLUDED_)

#if _MSC_VER > 1000
#pragma once
#endif // _MSC_VER > 1000
// DlgBackpropParameters.h : header file

// CDlgBackpropParameters dialog

class CDlgBackpropParameters : public CDialog
// Construction
	CDlgBackpropParameters(CWnd* pParent = NULL);   // standard constructor

// Dialog Data
	UINT	m_AfterEvery;
	double	m_EtaDecay;
	double	m_InitialEta;
	double	m_MinimumEta;
	CString	m_strInitialEtaMessage;
	CString	m_strStartingPatternNum;
	UINT	m_StartingPattern;
	UINT	m_cNumThreads;
	BOOL	m_bDistortPatterns;
	double	m_EstimatedCurrentMSE;

// Overrides
	// ClassWizard generated virtual function overrides
	virtual void DoDataExchange(CDataExchange* pDX);    // DDX/DDV support

// Implementation

	// Generated message map functions
		// NOTE: the ClassWizard will add member functions here

// Microsoft Visual C++ will insert additional declarations immediately before the previous line.

#endif // !defined(AFX_DLGBACKPROPPARAMETERS_H__58993721_5E08_4AB0_85D1_D00AE597CCC7__INCLUDED_)

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About the Author

Mike O'Neill
United States United States
Mike O'Neill is a patent attorney in Southern California, where he specializes in computer and software-related patents. He programs as a hobby, and in a vain attempt to keep up with and understand the technology of his clients.

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