Search Results - computational complexity theory

Theory of automata, formal languages and computation by Xavier, S. P. Eugene

Subjects: “…Computational complexity.…”
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Aspects of complexity minicourses in algorithmics, complexity and computational algebra : mathematics workshop, Kaikoura, January 7-15, 2000 /

Subjects: “…Computational complexity Congresses.…”
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Perspectives in logic logical foundations of proof complexity / by Cook, Stephen, 1948-

Subjects: “…Computational complexity.…”
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Foundations of complex systems nonlinear dynamics statistical physics, information and prediction / by Nicolis, G., 1939-

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Foundations of complex systems emergence, information and prediction / by Nicolis, G., 1939-

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Logic and automata history and perspectives /

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A nonlinear dynamics perspective of Wolfram's new kind of science. by Chua, Leon O., 1936-

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A nonlinear dynamics perspective of Wolfram's new kind of science by Chua, Leon O., 1936-

Subjects: An electronic book accessible through the World Wide Web; click to view

Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems

Subjects: “…Computational complexity.…”
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Complex adaptive systems an introduction to computational models of social life / by Miller, John H. (John Howard), 1959-

Table of Contents: “…Modeling -- On emergence -- pt. 3. COMPUTATIONAL MODELING. Computation as theory -- Why agent-based objects? …”
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Theory of lift introductory computational aerodynamics in MATLAB/OCTAVE / by McBain, G. D. (Geordie G.)

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Cloud computing theory and practice / by Marinescu, Dan C.

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Complex valued nonlinear adaptive filters noncircularity, widely linear, and neural models / by Mandic, Danilo P.

Subjects: “…Functions of complex variables.…”
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Modelling under risk and uncertainty an introduction to statistical, phenomenological and computational methods / by Rocquigny, Etienne de

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Financial simulation modeling in Excel a step-by-step guide / by Allman, Keith A., 1977-

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Dendrimer-based drug delivery systems from theory to practice /

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Adaptive control approach for software quality improvement

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Multicriteria decision aid and artificial intelligence links, theory and applications / by Doumpos, Michael

Table of Contents: “…Machine generated contents note: List of Contributors Preface Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview 1.1 Introduction 1.2 The MCDA Paradigm 1.2.1 Modeling Process 1.2.2 Methodological Approaches 1.3 Computational Intelligence in MCDA 1.3.1 Statistical Learning and Data Mining 1.3.2 Fuzzy Modeling 1.3.3 Metaheuristics 1.4 Conclusions References 2 Intelligent Decision Support Systems 2.1 Introduction 2.2 Fundamentals of Human Decision Making 2.3 Decision Support System 2.4 Intelligent Decision Support Systems 2.4.1 Artificial Neural Networks for Intelligent Decision Support 2.4.2 Fuzzy Logic for Intelligent Decision Support 2.4.3 Expert Systems for Intelligent Decision Support 2.4.4 Evolutionary Computing for Intelligent Decision Support 2.4.5 Intelligent Agents for Intelligent Decision Support 2.5 Evaluating Intelligent Decision Support Systems 2.5.1 Determining Evaluation Criteria 2.5.2 Multi-Criteria Model for IDSS Assessment 2.6 Summary and Future Trends References Part Two Intelligent Technologies for Decision Support and Preference Modeling 3 Designing Distributed Multi-Criteria Decision Support Systems for Complex and Uncertain Situations 3.1 Introduction 3.2 Example Applications 3.3 Key Challenges 3.4 Making Trade-offs: Multi-criteria Decision Analysis 3.4.1 Multi-attribute Decision Support 3.4.2 Making Trade-offs Under Uncertainty 3.5 Exploring the Future: Scenario-based Reasoning 3.6 Making Robust Decisions: Combining MCDA and SBR 3.6.1 Decisions Under Uncertainty: The Concept of Robustness 3.6.2 Combining Scenarios and MCDA 3.6.3 Collecting, Sharing and Processing Information: A Distributed Approach 3.6.4 Keeping Track of Future Developments: Constructing Comparable Scenarios 3.6.5 Respecting Constraints and Requirements: Scenario Management 3.6.6 Assisting Evaluation: Assessing Large Numbers of Scenarios 3.7 Discussion 3.8 Conclusion References 4 Preference Representation with Ontologies 4.1 Introduction 4.1.1 Structure of the Chapter 4.2 Ontology-based Preference Models 4.3 Maintaining the User's Profile up to Date 4.4 Decision Making Methods Exploiting the Preference Information Stored in Ontologies 4.4.1 Recommendation Based on Aggregation 4.4.2 Recommendation Based on Similarities 4.4.3 Recommendation Based on Rules 4.5 Discussion and Open Questions References Part Three Decision Models 5 Neural Networks in Multicriteria Decision Support 5.1 Introduction 5.2 Basic Concepts of Neural Networks 5.2.1 Neural Networks for Intelligent Decision Support 5.3 Basics in Multicriteria Decision Aid 5.3.1 MCDM Problems 5.3.2 Solutions of MCDM Problems 5.4 Neural Networks and Multicriteria Decision Support 5.4.1 Review of Neural Network Applications to MCDM Problems 5.4.2 Discussion 5.5 Summary and Conclusions References 6 Rule-Based Approach to Multicriteria Ranking 6.1 Introduction 6.2 Problem Setting 6.3 Pairwise Comparison Table (PCT) 6.4 Rough Approximation of Outranking and Non-outranking Relations 6.5 Induction and Application of Decision Rules 6.6 Exploitation of Preference Graphs 6.7 Illustrative Example 6.8 Summary and Conclusions References 7 About the Application of Evidence Theory in MultiCriteria Decision Aid 7.1 Introduction 7.2 Evidence Theory: Some Concepts 7.2.1 Knowledge Model 7.2.2 Combination 7.2.3 Decision Making 7.3 New Concepts in Evidence Theory for MCDA 7.3.1 First Belief Dominance 7.3.2 RBBD Concept 7.4 Multicriteria Methods modeled by Evidence Theory 7.4.1 Evidential Reasoning Approach 7.4.2 DS/AHP 7.4.3 DISSET 7.4.4 A Choice Model Inspired by ELECTRE I 7.4.5 A Ranking Model Inspired by Xu et al.'…”
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E-learning and the science of instruction proven guidelines for consumers and designers of multimedia learning / by Clark, Ruth Colvin

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Alan Turing his work and impact /

Table of Contents: “…pt. 1. How do we compute? what can we prove? -- pt. 2. Hiding and unhiding information : cryptology, complexity and number theory -- pt. 3. …”
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