We propose a new methodology to aid in designing a portjolio of investment over multiple stock markets. It is our hypothesis that financial stock market trends may be predicted better over a set of markets instead of any one single market. A selection criteria is proposed in this paper to make this choice effectively. This criteria is based upon the observed backpropagation and recurrent neural networks prediction accuracy, and the overall change recorded in the previous year. The results obtained when using data for four consecutive years over five international stock markets supports our claim. Backpropagation nehvorks use gradient descent to learn spatial relationships. On the other hand, recurrent networks are capable of capturing spatiotemporal information from training data. This paper analyzes application of recurrent networks to the stock market return prediction problem in contrast with backpropagation networks. On the basis of the results observed during these experiments it follows that the effect of learning temporal information was not substantial on the prediction accuracy for the stock market returns.
Pattern Recognition has become an attractive research oriented field of the computer vision and machine learning for the last few decades. Neural pattern recognition techniques are also being exercised for pattern recognition, showing promising results. In this paper, a comparison is made between statistical and neural pattern recognition techniques and tried to realize how neural techniques reveal far better results than statistical techniques. In this comparison, Discriminant Analysis (DA) and Principal Component Analysis (PCA) are used for pattern recognition, which are a statistical technique. Discriminant Analysis engrosses the problem of huge data dimensions and small sample size. To evade these problems, pattern recognition task is also implemented using Generalized Regression Neural Network (GRNN) and Back-propagation Neural Network (BPNN) techniques. The task of pattern recognition is conceded on a data base of face images of 400 people. Neural networks proved results for better than statistical methods.
This paper reports evaluations of several neural architectures when the handwritten character recognition is approached as a problem of spectro-temporal pattern recognition. In general, neural networks specialize in learning either the spectral or temporal characteristics of patterns. However, choice of appropriate features and architectures could lead to obtaining both spectral and temporal characteristics from the handwritten character patterns. One such feature and three appropriate architectures are the focus of this paper. The results obtained during a limited set of experiments indicate a great potential for the spectro-temporal approach to be a useful contender for being a part of schemes of handwritten character recognition systems. In addition, a simple voting method is presented for collaborative character recognition using three different recognition criteria.
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