By Demetrios Michalopoulos, Chih-Kang Hu (auth.), Tim Hendtlass, Moonis Ali (eds.)
Arti?cial Intelligence is a ?eld with an extended heritage, that's nonetheless greatly lively and constructing this day. advancements of latest and stronger thoughts, including the ever-increasing degrees of accessible computing assets, are fueling an expanding unfold of AI functions. those purposes, in addition to offering the commercial motive for the examine, additionally give you the impetus to extra increase the functionality of our concepts. This extra development this present day is probably to return from an knowing of the methods our structures paintings, and as a result in their barriers, instead of from rules ‘borrowed’ from biology. From this figuring out comes development; from development comes extra program; from extra program comes the chance to additional comprehend the restrictions, and so the cycle repeats itself inde?nitely. during this quantity are papers on a variety of themes; a few describe appli- tions which are in basic terms attainable due to contemporary advancements, others describe new advancements in basic terms simply being moved into functional program. the entire - pers re?ect the best way this ?eld maintains to force ahead. This convention is the fifteenth in an unbroken sequence of annual meetings on business and Engineering program of Arti?cial Intelligence and specialist structures equipped lower than the auspices of the overseas Society of utilized Intelligence.
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Additional info for Developments in Applied Artificial Intelligence: 15th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE 2002 Cairns, Australia, June 17–20, 2002 Proceedings
Girosi, P. Niyogi, T. Poggio, and V. Vapnik. Comparing support vector machines with Gaussian kernels to radial basis function classiﬁers. IEEE Trans. Signal Processing, 45(11):2758–2765, 1997. 42 7. Vladimir N. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, Heidelberg, DE, 1995. 38 8. D. W. Omlin. Automatic Detection of Image Orientation with Support Vector Machines. Technical Report TR-UWC-CS-01-01, University of the Western Cape, 2001. edu Abstract. In this paper, we propose an approach based on HMM and linguistics for the Vietnamese recognition problem, including handwritten and speech recognition.
1. A Comparison of Neural Networks with Time Series Models 31 volatility. According to Bollerslev, Engle and Nelson [8, 12, 9], it can be observed in most of the empirical applications of GARCH(1,1) model. 2 indicates that for the AR(9)EGARCH(1,1) model the leverage effect term (γ) is negative and statistically different from zero, indicating the existence of the leverage effect on IBOVESPA returns during the sample period. 294 confirms the existence of volatility clustering. 2 indicates that the model that presented lowest AIC and BIC was AR(9)EGARCH(1,1).
Toulson  presented a comparison between the multi-layer perceptron neural network and structural model by investigating the predictive power of both models for one-step-ahead forecast of the USD/DEM exchange rate. The results indicated that the multi-layer perceptron’s performance is superior to that of structural model. The aim of this article is to examine whether artificial neural networks can outperform traditional time series models for forecasting stock market returns in emerging markets.