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140122 ||| eng |
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|a 9781461515951
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|a Hanne, Thomas
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| 245 |
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|a Intelligent Strategies for Meta Multiple Criteria Decision Making
|h Elektronische Ressource
|c by Thomas Hanne
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| 250 |
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|a 1st ed. 2001
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| 260 |
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|a New York, NY
|b Springer US
|c 2001, 2001
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| 300 |
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|a XVIII, 197 p
|b online resource
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| 505 |
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|a 2. Method integration, openness, and object oriented implementation -- 3. A class concept for LOOPS -- 4. Problem solving and learning from an object oriented point of view -- 5. MADM methods in LOOPS -- 6. Neural networks in LOOPS -- 7. Neural MCDM networks in LOOPS -- 8. Evolutionary algorithms in LOOPS -- 9. An extended interactive framework -- 10. Summary and conclusions -- 6. Examples of the Application of Loops -- 1. Some remarks on the application of LOOPS -- 2. The learning of utility functions -- 3. Stock selection -- 4. Stock price prediction and the learning of time series -- 5. Stock analysis and long-term prediction -- 6. Method learning -- 7. Meta learning -- 8. An integrated proposal for the application of LOOPS -- 9. Summary and conclusions -- 7. Critical Resume and Outlook -- References -- Appendices -- A- Some basic concepts of MCDM theory -- 1. Relations -- 2. Efficiencyconcepts and scalarizing theorems --
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|a 3. Utility concepts and other axiomatics 166 B- Some selected MCDM methods -- 1. Simple additive weighting -- 2. Achievement levels -- 3. Reference point approaches -- 4. The outranking method PROMETHEE 171 C- Neural networks -- 1. Introduction to neural networks -- 2. Neural networks for intelligent decision support 178 D- Evolutionary algorithms -- 1. Introduction to evolutionary algorithms -- 2. The generalization of evolutionary algorithms 186 E- List of symbols 189 F- List of abbreviations
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|a 1. Introduction -- 1. MCDM problems -- 2. Solutions of MCDM problems -- 3. Decision processes and the application of MCDM methods -- 4. Concepts of ‘correct’ decision making in MCDM methods -- 5. Summary and conclusions -- 2. The Meta Decision Problem in MCDM -- 1. Methodological criticism in MCDM -- 2. The met a decision problem in MCDM -- 3. Summary and conclusions -- 3. Neural Networks and Evolutionary Learning For MCDM -- 1. Neural networks and MCDM -- 2. Evolutionary learning -- 3. Summary and conclusions -- 4. On the Combination of MCDM Methods -- 1. Introduction -- 2. Properties of MCDM methods -- 3. Properties of specific MCDM methods -- 4. Properties of neurons and neural networks -- 5. The combination of algorithms -- 6. Neural MCDM networks -- 7. Termination and runtime of the algorithm -- 8. Summary and conclusions -- 5. Loops - An Object Oriented DSS for Solving Meta Decision Problems -- 1. Preliminary remarks --
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| 653 |
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|a Operations research
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Operations Research and Decision Theory
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a International Series in Operations Research & Management Science
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| 028 |
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|a 10.1007/978-1-4615-1595-1
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| 856 |
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|u https://doi.org/10.1007/978-1-4615-1595-1?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 658.403
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| 520 |
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|a Multiple criteria decision-making research has developed rapidly and has become a main area of research for dealing with complex decision problems which require the consideration of multiple objectives or criteria. Over the past twenty years, numerous multiple criterion decision methods have been developed which are able to solve such problems. However, the selection of an appropriate method to solve a particular decision problem is today's problem for a decision support researcher and decision-maker. Intelligent Strategies for Meta Multiple Criteria Decision-Making deals centrally with the problem of the numerous MCDM methods that can be applied to a decision problem. The book refers to this as a `meta decision problem', and it is this problem that the book analyzes. The author provides two strategies to help the decision-makers select and design an appropriate approach to a complex decision problem. Either of these strategies can be designed into a decision support system itself. One strategy is to use machine learning to design an MCDM method. This is accomplished by applying intelligent techniques, namely neural networks as a structure for approximating functions and evolutionary algorithms as universal learning methods. The other strategy is based on solving the meta decision problem interactively by selecting or designing a method suitable to the specific problem, for example, the constructing of a method from building blocks. This strategy leads to a concept of MCDM networks. Examples of this approach for a decision support system explain the possibilities of applying the elaborated techniques and their mutual interplay. The techniques outlined in the book can be used by researchers, students, and industry practitioners to better model and select appropriate methods for solving complex, multi-objective decision problems
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