Incremental Learning Approach for Enhancing the Performance of Multi-Layer Perceptron for Determining the Stock Trend

This paper introduces a new technique for achieving minimum risk of predicting stock trend using multi-layer perceptron. The proposed technique presents the method of classification the stock trend .the paper show a comparison among multi-layer perceptron, gene learning theory. The achieved results show the superior performance of the multi-layer perceptron which is based on mathematical back ground.

[1]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[2]  Yuehui Chen,et al.  Stock Index Modeling using EDA based Local Linear Wavelet Neural Network , 2005, 2005 International Conference on Neural Networks and Brain.

[3]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[4]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[5]  David Enke,et al.  The adaptive selection of financial and economic variables for use with artificial neural networks , 2004, Neurocomputing.

[6]  Jason Weston,et al.  Large-scale kernel machines , 2007 .

[7]  Olivier Chapelle,et al.  Training a Support Vector Machine in the Primal , 2007, Neural Computation.

[8]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[10]  Foster Provost,et al.  Machine Learning from Imbalanced Data Sets 101 , 2008 .

[11]  Bernhard Schölkopf,et al.  Kernel Methods in Computational Biology , 2005 .

[12]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Carl E. Rasmussen,et al.  The Need for Open Source Software in Machine Learning , 2007, J. Mach. Learn. Res..

[15]  Dennis Olson,et al.  Neural network forecasts of Canadian stock returns using accounting ratios , 2003 .

[16]  William Stafford Noble,et al.  Support vector machine , 2013 .