Neural network rule extraction for gaining insight into the characteristics of poverty

[1]  Manuel A. Fernández-Gámez,et al.  Corporate reputation and market value: Evidence with generalized regression neural networks , 2016, Expert Syst. Appl..

[2]  Khashayar Khorasani,et al.  Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis , 2015, Neural Computing and Applications.

[3]  Zaher Mundher Yaseen,et al.  RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia , 2015, Neural Computing and Applications.

[4]  Turker Tekin Erguzel,et al.  Artificial intelligence approach to classify unipolar and bipolar depressive disorders , 2015, Neural Computing and Applications.

[5]  S. P. Ghoshal,et al.  Dynamic electricity price forecasting using local linear wavelet neural network , 2015, Neural Computing and Applications.

[6]  G. Köhlin,et al.  The Persistence of Subjective Poverty in Urban Ethiopia , 2014 .

[7]  Springer-Verlag London,et al.  Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India , 2014 .

[8]  Daniel Neff,et al.  Fuzzy set theoretic applications in poverty research , 2013 .

[9]  The Subjective Approach as a Tool for Understanding Poverty: The Case of Senegal , 2013 .

[10]  Sharmishtha Mitra,et al.  Early warning prediction system for high inflation: an elitist neuro-genetic network model for the Indian economy , 2013, Neural Computing and Applications.

[11]  A. Chapoto,et al.  Poverty and Prime-Age Mortality in Eastern and Southern Africa: Evidence from Zambia and Kenya , 2012 .

[12]  Zhongwei Xing,et al.  Impact of education and health on poverty reduction: Monetary and non-monetary evidence from Fiji , 2012 .

[13]  Duy Vương Quốc.  Determinants of household access to formal credit in the rural areas of the Mekong Delta, Vietnam , 2012 .

[14]  Tobia Lakes,et al.  Comparing Support Vector Regression and Statistical Linear Regression for Predicting Poverty Incidence in Vietnam , 2012, AGILE Conf..

[15]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[16]  Besma Belhadj,et al.  New fuzzy indices of poverty by distinguishing three levels of poverty , 2011 .

[17]  K. L. Maharjan,et al.  Determinants of household food security in Nepal: A binary logistic regression analysis , 2011 .

[18]  Andrew McKay,et al.  A quantitative analysis of poverty and livelihood profiles: The case of rural Rwanda , 2010 .

[19]  Berna Yazici,et al.  Analysis of international debt problem using artificial neural networks and statistical methods , 2010, Neural Computing and Applications.

[20]  N. Khadioli,et al.  A Logistic Regression Model to Identify Key Determinants of Poverty Using Demographic and Health Survey Data , 2010 .

[21]  Suresh Chandra Babu,et al.  Food Security, Poverty and Nutrition Policy Analysis: Statistical Methods and Applications , 2009 .

[22]  Mukta Paliwal,et al.  Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..

[23]  Paulo J. G. Lisboa,et al.  The Use of Artificial Neural Networks in Decision Support in Cancer: a Systematic Review , 2005 .

[24]  Jordan Chamberlin,et al.  An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi , 2005 .

[25]  Johannes Hoogeveen,et al.  Measuring Welfare for Small but Vulnerable Groups: Poverty and Disability in Uganda , 2004 .

[26]  Kin Keung Lai,et al.  Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review , 2004, Int. J. Inf. Technol. Decis. Mak..

[27]  S. Klasen,et al.  Determinants of Income Mobility and Household Poverty Dynamics in South Africa , 2004, SSRN Electronic Journal.

[28]  Zhongya Zhang,et al.  Artificial neural networks applied to polymer composites: a review , 2003 .

[29]  David Bigman,et al.  Geographical targeting of poverty alleviation programs: methodology and applications in rural India , 2002 .

[30]  Paulo J. G. Lisboa,et al.  A review of evidence of health benefit from artificial neural networks in medical intervention , 2002, Neural Networks.

[31]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[32]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[33]  I. Flood,et al.  Neural networks in civil engineering: a review , 2001 .

[34]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[35]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[36]  Alfredo Vellido,et al.  Neural networks in business: a survey of applications (1992–1998) , 1999 .

[37]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[38]  M. Ravallion.  Poverty lines in theory and practice , 1998 .

[39]  Rudy Setiono,et al.  A Penalty-Function Approach for Pruning Feedforward Neural Networks , 1997, Neural Computation.

[40]  Huan Liu,et al.  Symbolic Representation of Neural Networks , 1996, Computer.

[41]  Huan Liu,et al.  X2R: a fast rule generator , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[42]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[43]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.