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Applied Intelligent Systems: New Directions 2004 ed. [Kõva köide]

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  • Formaat: Hardback, 325 pages, kõrgus x laius: 235x155 mm, kaal: 751 g, XVII, 325 p., 1 Hardback
  • Sari: Studies in Fuzziness and Soft Computing 153
  • Ilmumisaeg: 05-May-2004
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540211535
  • ISBN-13: 9783540211532
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Applied Intelligent Systems: New Directions 2004 ed.Suurem pilt
  • Formaat: Hardback, 325 pages, kõrgus x laius: 235x155 mm, kaal: 751 g, XVII, 325 p., 1 Hardback
  • Sari: Studies in Fuzziness and Soft Computing 153
  • Ilmumisaeg: 05-May-2004
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540211535
  • ISBN-13: 9783540211532
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783540211532.html
Märksõnad:
Humans have always been hopeless at predicting the futuremost 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 wont 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.
1 Adaptive Technical Analysis in the Financial Markets Using Machine Learning: a Statistical View
David Edelman and Pam Davy
1.1 'Technical Analysis' in Finance: a Brief Background
1(1)
1.2 The 'Moving Windows' Paradigm
2(1)
1.3 Post-Hoc Performance Assessment
3(4)
1.3.1 The Effect of Dividends
5(1)
1.3.2 Transaction Costs Approximations
6(1)
1.4 Genetic programming
7(3)
1.5 Support-Vector Machines
10(2)
1.6 Neural Networks
12(2)
1.7 Discussion
14(1)
References
15(2)
2 Higher Order Neural Networks for Satellite Weather Prediction
Ming Zhang and John Fulcher
2.1 Introduction
17(1)
2.2 Higher Order Neural Networks
18(15)
2.2.1 Polynomial Higher-Order Neural Networks
20(3)
2.2.2 Trigonometric Higher-Order Neural Networks
23(7)
Output Neurons in THONN Model#1
24(3)
Second Hidden Layer Neurons in THONN Model#l
27(3)
First Hidden Layer Neurons in THONN Model#1
30(1)
2.2.3 Neuron-Adaptive Higher-Order Neural Network
30(3)
2.3 Artificial Neural Network Groups
33(2)
2.3.1 ANN Groups
33(1)
2.3.2 PHONN, THONN & NAHONN Groups
34(1)
2.4 Weather Forecasting & ANNs
35(1)
2.5 HONN Models for Half-hour Rainfall Prediction
36(3)
2.5.1 PT-HONN Model
36(1)
2.5.2 A-PHONN Model
37(1)
2.5.3 M-PHONN Model
38(1)
2.5.4 Satellite Rainfall Estimation Results
38(1)
2.6 ANSER System for Rainfall Estimation
39(8)
2.6.1 ANSER Architecture
40(1)
2.6.2 ANSER Operation
41(2)
2.6.3 Reasoning Network Based on ANN Groups
43(2)
2.6.4 Rainfall Estimation Results
45(2)
2.7 Summary
47(1)
Acknowledgements
47(1)
References
47(4)
Appendix-A Second Hidden Layer (multiply) Neurons
51(3)
Appendix-B First Hidden Layer Neurons
54(5)
3 Independent Component Analysis
Andrew Back
3.1 Introduction
59(1)
3.2 Independent Component Analysis Methods
60(15)
3.2.1 Basic Principles and Background
60(2)
3.2.2 Mutual Information Methods
62(2)
3.2.3 InfoMax ICA Algorithm
64(1)
3.2.4 Natural/Relative Gradient Methods
65(1)
3.2.5 Extended InfoMax
66(1)
3.2.6 Adaptive Mutual Information
66(2)
3.2.7 Fixed Point ICA Algorithm
68(1)
3.2.8 Decorrelation and Rotation Methods
69(2)
3.2.9 Comon Decorrelation and Rotation Algorithm
71(1)
3.2.10 Temporal Decorrelation Methods
71(1)
3.2.11 Molgedey and Schuster Temporal Correlation Algorithm
72(1)
3.2.12 Spatio-temporal ICA Methods
73(1)
3.2.13 Cumulant Tensor Methods
74(1)
3.2.14 Nonlinear Decorrelation Methods
75(1)
3.3 Applications of ICA
75(1)
3.3.1 Guidelines for Applications of ICA
75(1)
3.3.2 Biomedical Signal Processing
76(1)
3.3.3 Extracting Speech from Noise
77(1)
3.3.4 Unsupervised Classification Using ICA
78(1)
3.3.5 Computational Finance
81(2)
3.4 Open Problems for ICA Research
83(2)
3.5 Summary
85(1)
References
86(9)
Appendix - Selected ICA Resources
95(2)
4 Regulatory Applications of Artificial Intelligence
Howard Copland
4.1 Introduction
97(1)
4.2 Solution Spaces, Data and Mining
98(4)
4.3 Artificial Intelligence in Context
102(2)
4.4 Anomaly Detection: ANNs for Prediction/Classification
104(1)
4.4.1 Training to Classify on Spare Data Sets
105(1)
4.4.2 Training to Predict on Dense Data Sets
106(1)
4.4.3 Feature Selection for and Performance of Anomaly Detection Suites
109(1)
4.4.4 Interpreting Anomalies
113(1)
4.4.5 Other Approaches to Anomaly Detection
115(1)
4.4.6 Variations of BackProp' ANNs for Use with Complex Data Sets
120(1)
4.5 Formulating Expert Systems to Identify Common Events of Interest
121(9)
A Note on the Software
130(1)
Acknowledgements
130(1)
References
130(3)
5 An Introduction to Collective Intelligence
Tim Hendtlass
5.1 Collective Intelligence
133(1)
5.1.1 A Simple Example of Stigmergy at Work
134(2)
5.2 The Power of Collective Action
136(2)
5.3 Optimisation
138(1)
5.3.1 Optimisation in General
138(1)
5.3.2 Shades of Optimisation
139(1)
5.3.3 Exploitation versus Exploration
139(1)
5.3.4 Example of Common Optimisation Problems
140(1)
Minimum Path Length
140(1)
Function Optimisation
140(1)
Sorting
141(1)
Multi-Component Optimisation
141(1)
5.4 Ant Colony Optimisation
141(1)
5.4.1 Ant Systems - the Basic Algorithm
143(22)
The Problem with AS
143(1)
5.4.2 Ant Colony Systems
144(1)
5.4.3 Ant Multi-Tour System (AMTS)
145(1)
5.4.4 Limiting the Pheromone Density - the Max-Min Ant System
145(1)
5.4.5 An Example: Using Ants to Solve a (simple) TSP
146(8)
5.4.6 Practical Considerations
154(1)
5.4.7 Adding a Local Heuristic
155(2)
5.4.8 Other Uses for ACO
157(1)
5.4.9 Using Ants to Sort
158(4)
An Example of Sorting Using ACO
162(3)
5.5 Particle Swarm Optimisation
165(1)
5.5.1 The Basic Particle Swarm Optimisation Algorithm
165(1)
5.5.2 Limitations of the Basic Algorithm
166(1)
5.5.3 Modifications to the Basic PSO Algorithm
167(12)
Choosing the Position S
167(1)
The Problem of a finite t
168(1)
Aggressively Searching Swarms
168(1)
Adding Memory to Each Particle
169(1)
5.5.4 Performance
170(1)
5.5.5 Solving TSP Problems Using PSO
171(1)
PSO Performance on a TSP
172(3)
5.5.6 Practical Considerations
175(1)
5.5.7 Scalability and Adaptability
176(1)
References
177(2)
6 Where are all the Mobile Robots?
Phillip McKerrow
6.1 Introduction
179(2)
6.2 Commercial Applications
181(1)
6.2.1 Robot Couriers
182(1)
6.2.2 Robot Vacuum Cleaners
183(1)
6.2.3 Robot Lawn Mowers
187(1)
6.2.4 Robot Pool Cleaners
189(1)
6.2.5 Robot People Transporter
191(1)
6.2.6 Robot Toys
193(1)
6.2.7 Other Applications
195(1)
6.2.8 Getting a Robot to Market
195(1)
6.2.9 Wheeled Mobile Robot Research
196(1)
6.3 Research Directions
197(1)
6.4 Conclusion
198(1)
A Note on the Figures
199(1)
References
199(2)
7 Building Intelligent Legal Decision Support Systems: Past Practice and Future Challenges
John Zeleznikow
7.1 Introduction
201(1)
7.1.1 Benefits of Legal Decision Support Systems to the Legal Profession
202(1)
7.1.2 Current Research in AI and Law
204(4)
7.2 Jurisprudential Principles for Developing Intelligent Legal Knowledge-Based Systems
208(1)
7.2.1 Reasoning with Open Texture
209(1)
7.2.2 The Inadequacies of Modelling Law as a Series of Rules
210(1)
7.2.3 Landmark and Commonplace Cases
211(3)
7.3 Early Legal Decision Support Systems
214(1)
7.3.1 Rule-Based Reasoning
214(1)
7.3.2 Case-Based Reasoning and Hybrid Systems
219(1)
7.3.3 Knowledge Discovery in Legal Databases
222(1)
7.3.4 Evaluation of Legal Knowledge-Based Systems
222(1)
7.3.5 Explanation and Argumentation in Legal Knowledge-Based Systems
231(2)
7.4 Legal Decision Support on the World Wide Web
233(1)
7.4.1 Legal Knowledge on the WWW
233(1)
7.4.2 Legal Ontologies
234(1)
7.4.3 Negotiation Support Systems
240(6)
7.5 Conclusion
246(1)
Acknowledgements
247(1)
References
247(8)
8 Forming Human-Agent Teams within Hostile Environments
Christos Sioutis, Pierre Urlings, Jeffrey Tweedale, and Nikhil Ichalkaranje
8.1 Introduction
255(1)
8.2 Background
256(1)
8.3 Cognitive Engineering
257(1)
8.4 Research Challenge
258(1)
8.4.1 Human-Agent Teaming
258(1)
8.4.2 Agent Learning
260(2)
8.5 The Research Environment
262(1)
8.5.1 The Concept of Situational Awareness
262(1)
8.5.2 The Unreal Tournament Game Platform
263(1)
8.5.3 The Jack Agent
263(1)
8.6 The Research Application
264(1)
8.6.1 The Human Agent Team
264(1)
8.6.2 The Simulated World Within Unreal Tournament
265(1)
8.6.3 Interacting With Unreal Tournament
267(1)
8.6.4 The Java Extension
268(1)
8.6.5 The Jack Component
269(1)
8.7 Demonstration System
270(1)
8.7.1 Wrapping Behaviours in Capabilities
271(1)
8.7.2 The Exploring Behaviours
272(1)
8.7.3 The Defending Behaviour
273(2)
8.8 Conclusions
275(1)
Acknowledgements
276(1)
References
276(5)
9 Fuzzy Multivariate Auto-Regression Method and its Application
N Arzu Sisman-Yilmaz, Ferda NAlpaslan and Lakhmi C Jain
9.1 Introduction
281(1)
9.2 Fuzzy Data Analysis
282(1)
9.2.1 Fuzzy Regression
282(1)
9.2.2 Fuzzy Time Series Analysis
284(1)
9.2.3 Fuzzy Linear Regression (FLR)
285(1)
Basic Definitions
285(1)
Linear Programming Problem
285(1)
9.3 Fuzzy Multivariate Auto-Regression Algorithm
286(9)
Example - Gas Furnace Data Processed by MAR
287(1)
9.3.1 Model Selection
288(1)
9.3.2 Motivation for FLR in Fuzzy MAR
289(1)
9.3.3 Fuzzification of Multivariate Auto-Regression
290(1)
9.3.4 Bayesian Information Criterion in Fuzzy MAR
291(1)
9.3.5 Obtaining a Linear Function for a Variable
292(1)
9.3.6 Processing of Multivariate Data
293(2)
9.4 Experimental Results
295(1)
9.4.1 Experiments with Gas Furnace Data
295(1)
9.4.2 Experiments with Interest Rate Data
296(1)
9.4.3 Discussion of Experimental Results
298(1)
9.5 Conclusions
299(1)
References
299(2)
10 Selective Attention Adaptive Resonance theory and Object Recognition
Peter Lozo, Jason Westmacott, Quoc V Do, Lakhmi C Jain and Lai Wu
10.1 Introduction
301(1)
10.2 Adaptive Resonance Theory (ART)
302(1)
10.2.1 Limitations of ART's Attentional Subsystem with Cluttered Inputs
303(2)
10.3 Selective Attention Adaptive Resonance Theory
305(1)
10.3.1 Neural Network Implementation of SAART
306(12)
Postsynaptic Cellular Activity
309(1)
Excitatory Postsynaptic Potential
309(1)
Lateral Competition
309(1)
Transmitter Dynamics
310(2)
10.3.2 Translation-invariant 2D Shape Recognition
312(5)
10.4 Conclusions
317(1)
References
318(3)
Index 321