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Applied Intelligent Systems

New Directions

Medium: Buch
ISBN: 978-3-642-05942-1
Verlag: Springer
Erscheinungstermin: 07.12.2010
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Humans have always been hopeless at predicting the future…most people now generally agree that the margin of viability in prophecy appears to be 1 ten years. Even sophisticated research endeavours in this arena tend to go 2 off the rails after a decade or so. The computer industry has been particularly prone to bold (and often way off the mark) predictions, for example: ‘I think there is a world market for maybe five computers’ Thomas J. Watson, IBM Chairman (1943), ‘I have traveled the length and breadth of this country and talked with the best people, and I can assure you that data processing is a fad that won’t last out the year’ Prentice Hall Editor (1957), ‘There is no reason why anyone would want a computer in their home’ Ken Olsen, founder of DEC (1977) and ‘640K ought to be enough for anybody’ Bill Gates, CEO Microsoft (1981). 3 The field of Artificial Intelligence – right from its inception – has been particularly plagued by ‘bold prediction syndrome’, and often by leading practitioners who should know better. AI has received a lot of bad press 4 over the decades, and a lot of it deservedly so. How often have we groaned in despair at the latest ‘by the year-20xx, we will all have…(insert your own particular ‘hobby horse’ here – e. g.

Produkteigenschaften

  • Artikelnummer: 9783642059421
  • Medium: Buch
  • ISBN: 978-3-642-05942-1
  • Verlag: Springer
  • Erscheinungstermin: 07.12.2010
  • Sprache(n): Englisch
  • Auflage: 1. Auflage. Softcover version of original hardcover Auflage 2004
  • Serie: Studies in Fuzziness and Soft Computing
  • Produktform: Kartoniert, Previously published in hardcover
  • Gewicht: 522 g
  • Seiten: 325
  • Format (B x H x T): 155 x 235 x 19 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

1 Adaptive Technical Analysis in the Financial Markets Using Machine Learning: a Statistical View.- 1.1 ‘Technical Analysis’ in Finance: a Brief Background.- 1.2 The ‘Moving Windows’ Paradigm.- 1.3 Post-Hoc Performance Assessment.- 1.4 Genetic programming.- 1.5 Support-Vector Machines.- 1.6 Neural Networks.- 1.7 Discussion.- References.- 2 Higher Order Neural Networks for Satellite Weather Prediction.- 2.1 Introduction.- 2.2 Higher Order Neural Networks.- 2.3 Artificial Neural Network Groups.- 2.4 Weather Forecasting & ANNs.- 2.5 HONN Models for Half-hour Rainfall Prediction.- 2.6 ANSER System for Rainfall Estimation.- 2.7 Summary.- 3 Independent Component Analysis.- 3.1 Introduction.- 3.2 Independent Component Analysis Methods.- 3.3 Applications of ICA.- 3.4 Open Problems for ICA Research.- 3.5 Summary.- References.- Appendix — Selected ICA Resources.- 4 Regulatory Applications of Artificial Intelligence.- 4.1 Introduction.- 4.2 Solution Spaces, Data and Mining.- 4.3 Artificial Intelligence in Context.- 4.4 Anomaly Detection: ANNs for Prediction/Classification.- 4.5 Formulating Expert Systems to Identify Common Events of Interest.- A Note on the Software.- Acknowledgements.- References.- 5 An Introduction to Collective Intelligence.- 5.1 Collective Intelligence.- 5.2 The Power of Collective Action.- 5.3 Optimisation.- 5.4 Ant Colony Optimisation.- 5.5 Particle Swarm Optimisation.- References.- 6 Where are all the Mobile Robots?.- 6.1 Introduction.- 6.2 Commercial Applications.- 6.3 Research Directions.- 6.4 Conclusion.- A Note on the Figures.- References.- 7 Building Intelligent Legal Decision Support Systems: Past Practice and Future Challenges.- 7.1 Introduction.- 7.2 Jurisprudential Principles for Developing Intelligent Legal Knowledge-Based Systems.- 7.3Early Legal Decision Support Systems.- 7.4 Legal Decision Support on the World Wide Web.- 7.5 Conclusion.- Acknowledgements.- References.- 8 Forming Human-Agent Teams within Hostile Environments.- 8.1 Introduction.- 8.2 Background.- 8.3 Cognitive Engineering.- 8.4 Research Challenge.- 8.5 The Research Environment.- 8.6 The Research Application.- 8.7 Demonstration System.- 8.8 Conclusions.- Acknowledgements.- References.- 9 Fuzzy Multivariate Auto-Regression Method and its Application.- 9.1 Introduction.- 9.2 Fuzzy Data Analysis.- 9.3 Fuzzy Multivariate Auto-Regression Algorithm.- 9.4 Experimental Results.- 9.5 Conclusions.- References.- 10 Selective Attention Adaptive Resonance theory and Object Recognition.- 10.1 Introduction.- 10.2 Adaptive Resonance Theory (ART).- 10.3 Selective Attention Adaptive Resonance Theory.- 10.4 Conclusions.- References.