Abstract
Simplicity and effectiveness guide commercial and government applications of Artificial Intelligence, with a strong emphasis on the simplicity. Basically, if a system is too complicated or demands too high a level of maintenance, then it will probably fail in most commercial or government environments. Furthermore, if a moderate level of understanding of the solution ‘engine’ is required in order to interpret the results, then it will also probably fail in most commercial or government environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baum E and Haussler D (1989) What size net gives valid generalization? Neural Computation, 1(1): 151–160.
Berger G and Tuzhilin A (1998) Discovering unexpected patterns in temporal data using temporal logic, in Etzion O, Jajodia S and Sripada S (eds) Temporal Databases–Research and Practice (Lecture Notes in Computer Science 1399), Springer-Verlag, Berlin: 281–309.
Berndt DJ and Clifford J (1996) Finding Patterns Dynamic Programming Approach, in Fayyad UM, Piatetsky-Shapiro G, Smyth P, and Uthursamy R (eds) Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA: 229–248.
Bors AG (2001) Introduction to the Radial Basis Function (RBF) Networks, Online Symposium for Electronics Engineers, 1(1), DSP Algorithms: Multimedia,http://www.osee.net/ February 13: 1–7.
Bors AG and Pitas I (2001) Robust RBF Networks, in Howlett RJ and Jain LC (eds), Radial basis function neural networks: design and applications, Physica-Verlag, Heidelberg: 125–153.
Box GEP, Jenkins GM and Reinsel GC (1994) Time Series Analysis, Forecasting and Control (3’ d ed), Prentice Hall, Englewood Cliffs, NJ.
Hartigan JA and Wong MA (1978) Algorithm 136. A k-means clustering algorithm, Applied Statistics, 28: 100.
Keogh E and Smyth P (1997) A Probabilistic Adpproach to Fast Pattern Matching in Time Series Databases, Proc 3’ Intl Conf Knowledge Discovery and Data Mining, Newport Beach, CA, 14–17 August: 20–24.
Loh W-Y and Shih Y-S (1997) Split selection methods for classification trees, Statistica Sinica, 7: 815–840.
Murray G and Hendtlass T (2001) Enhanced Artificial Neurons for Network Applications, Proc 14` h Intl Conf Engineering & Industrial Applications AI & Expert Systems, Budapest, 4–7 June, (Lecture Notes in Artificial Intelligence 2070), Springer-Verlag, Berlin: 281–289.
Philpot D and Hendtlass T (1996) A Cascade of Neural Networks for Complex Classification, Proc 9` h Intl Conf Engineering & Industrial Applications AI, Fukuoka, 4–7 June, Springer-Verlag, Berlin: 791.
Philpot D and Hendtlass T (1997) Ensembles of Networks for Digital Problems, Proc 3rd Intl Conf Artificial Neural Networks and Genetic Algorithms (ICANNGA97), Norwich, 1–4 April, Springer-Verlag, Berlin: 31–34.
Philpot D and Hendtlass T (1998) Ensembles of Networks for Analogue Problems, Proc 11 `h IEA/AIE Conf (Methodology and Tools in Knowledge-Based Systems), Benicassim, 1–4 June, Springer-Verlag, Berlin: 625–631.
Povinelli RJ (2000) Using Genetic Algorithms to Find Temporal Patterns Indicative of Time Series Events, Proc GECCO 2000 Workshop: Data Mining with Evolutionary Algorithms, Las Vegas, Nevada, 8–12 July: 8084.
Povinelli RJ and Feng X (2003) A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events, IEEE Trans Knowledge and Data Engineering, 15 (2): 339–352.
Sarle WS (1994) Neural Networks and Statistical Models, Proc 19th Annual SAS Users Group Intl Conf, Cary, NC, 10–13 April: 1538–1550.
Sharkey AJC (1996) On Combining Artificial Neural Networks, Connection Science, 8: 299–314.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Copland, H. (2004). Regulatory Applications of Artificial Intelligence. In: Fulcher, J., Jain, L.C. (eds) Applied Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39972-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-39972-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05942-1
Online ISBN: 978-3-540-39972-8
eBook Packages: Springer Book Archive