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1. |
Record Nr. |
UNISA996466349103316 |
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Titolo |
Evolutionary Multi-Criterion Optimization [[electronic resource] ] : Second International Conference, EMO 2003, Faro, Portugal, April 8-11, 2003, Proceedings / / edited by Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
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ISBN |
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Edizione |
[1st ed. 2003.] |
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Descrizione fisica |
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1 online resource (XVI, 820 p.) |
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Collana |
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Lecture Notes in Computer Science, , 0302-9743 ; ; 2632 |
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Disciplina |
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Soggetti |
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Evolutionary biology |
Software engineering |
Algorithms |
Numerical analysis |
Computer science—Mathematics |
Artificial intelligence |
Evolutionary Biology |
Software Engineering/Programming and Operating Systems |
Algorithm Analysis and Problem Complexity |
Numeric Computing |
Discrete Mathematics in Computer Science |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Objective Handling and Problem Decomposition -- The Maximin Fitness Function; Multi-objective City and Regional Planning -- Conflict, Harmony, and Independence: Relationships in Evolutionary Multi-criterion Optimisation -- Is Fitness Inheritance Useful for Real-World Applications? -- Use of a Genetic Heritage for Solving the Assignment Problem with Two Objectives -- Fuzzy Optimality and Evolutionary Multiobjective Optimization -- IS-PAES: A Constraint-Handling Technique Based on Multiobjective Optimization Concepts -- |
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A Population and Interval Constraint Propagation Algorithm -- Multi-objective Binary Search Optimisation -- Covering Pareto Sets by Multilevel Evolutionary Subdivision Techniques -- An Adaptive Divide-and-Conquer Methodology for Evolutionary Multi-criterion Optimisation -- Multi-level Multi-objective Genetic Algorithm Using Entropy to Preserve Diversity -- Solving Hierarchical Optimization Problems Using MOEAs -- Multiobjective Meta Level Optimization of a Load Balancing Evolutionary Algorithm -- Algorithm Improvements -- Schemata-Driven Multi-objective Optimization -- A Real-Coded Predator-Prey Genetic Algorithm for Multiobjective Optimization -- Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions -- Trade-Off between Performance and Robustness: An Evolutionary Multiobjective Approach -- Online Adaptation -- The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization -- Self-Adaptation for Multi-objective Evolutionary Algorithms -- MOPED: A Multi-objective Parzen-Based Estimation of Distribution Algorithm for Continuous Problems -- Test Problem Construction -- Instance Generators and Test Suites for the Multiobjective Quadratic Assignment Problem -- Dynamic Multiobjective Optimization Problems: Test Cases, Approximation, and Applications -- No Free Lunch and Free Leftovers Theorems for Multiobjective Optimisation Problems -- Performance Analysis and Comparison -- A New MOEA for Multi-objective TSP and Its Convergence Property Analysis -- Convergence Time Analysis for the Multi-objective Counting Ones Problem -- Niche Distributions on the Pareto Optimal Front -- Performance Scaling of Multi-objective Evolutionary Algorithms -- Searching under Multi-evolutionary Pressures -- Minimal Sets of Quality Metrics -- A Comparative Study of Selective Breeding Strategies in a Multiobjective Genetic Algorithm -- An Empirical Study on the Effect of Mating Restriction on the Search Ability of EMO Algorithms -- Alternative Methods -- Using Simulated Annealing and Spatial Goal Programming for Solving a Multi Site Land Use Allocation Problem -- Solving Multi-criteria Optimization Problems with Population-Based ACO -- A Two-Phase Local Search for the Biobjective Traveling Salesman Problem -- Implementation -- PISA — A Platform and Programming Language Independent Interface for Search Algorithms -- A New Data Structure for the Nondominance Problem in Multi-objective Optimization -- The Measure of Pareto Optima Applications to Multi-objective Metaheuristics -- Distributed Computing of Pareto-Optimal Solutions with Evolutionary Algorithms -- Applications -- Multiobjective Capacitated Arc Routing Problem -- Multi-objective Rectangular Packing Problem and Its Applications -- Experimental Genetic Operators Analysis for the Multi-objective Permutation Flowshop -- Modification of Local Search Directions for Non-dominated Solutions in Cellular Multiobjective Genetic Algorithms for Pattern Classification Problems -- Effects of Three-Objective Genetic Rule Selection on the Generalization Ability of Fuzzy Rule-Based Systems -- Identification of Multiple Gene Subsets Using Multi-objective Evolutionary Algorithms -- Non-invasive Atrial Disease Diagnosis Using Decision Rules: A Multi-objective Optimization Approach -- Intensity Modulated Beam Radiation Therapy Dose Optimization with Multiobjective Evolutionary Algorithms -- Multiobjective Evolutionary Algorithms Applied to the Rehabilitation of a Water Distribution System: A Comparative Study -- Optimal Design of Water Distribution System by Multiobjective Evolutionary Methods -- Evolutionary Multiobjective Optimization in Watershed Water Quality Management -- Different Multi-objective Evolutionary Programming Approaches for Detecting Computer Network Attacks -- Safety Systems |
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Optimum Design by Multicriteria Evolutionary Algorithms -- Applications of a Multi-objective Genetic Algorithm to Engineering Design Problems -- A Real-World Test Problem for EMO Algorithms -- Genetic Methods in Multi-objective Optimization of Structures with an Equality Constraint on Volume -- Multi-criteria Airfoil Design with Evolution Strategies -- Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map. |
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2. |
Record Nr. |
UNINA9910482786303321 |
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Autore |
Stub Iver <1611.> |
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Titolo |
De oratione erudita et latina theses, præside Iuaro Stubbæo, respondente Nicolao Petri Nestuadense [[electronic resource]] |
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Pubbl/distr/stampa |
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Copenhagen, : Matthiæ Vinitor, 1590 |
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Descrizione fisica |
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Online resource ([6] bl.) |
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Altri autori (Persone) |
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PetraeusNicolaus <1601-1634.> |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Reproduction of original in Det Kongelige Bibliotek / The Royal Library (Copenhagen). |
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3. |
Record Nr. |
UNINA9910784592903321 |
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Titolo |
Outcome prediction in cancer [[electronic resource] /] / editors, Azzam F.G. Taktak and Anthony C. Fisher |
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Pubbl/distr/stampa |
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Amsterdam ; ; Boston, : Elsevier, 2007 |
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ISBN |
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1-280-74728-5 |
9786610747283 |
0-08-046803-9 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (483 p.) |
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Altri autori (Persone) |
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TaktakAzzam F. G |
FisherAnthony C., Dr. |
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Disciplina |
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Soggetti |
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Cancer - Diagnosis |
Cancer - Prognosis |
Neural networks (Computer science) |
Survival analysis (Biometry) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Front cover; Title page; Copyright page; Foreword; Table of Contents; Contributors; Introduction; Section 1: The Clinical Problem; Chapter 1: The Predictive Value of Detailed Histological Staging of Surgical Resection Specimens in Oral Cancer; 1. INTRODUCTION; 2. PREDICTIVE FEATURES RELATED TO THE PRIMARY TUMOUR; 3. PREDICTIVE FEATURES RELATED TO THE REGIONAL LYMPH NODES; 4. DISTANT (SYSTEMIC) METASTASES; 5. GENERAL PATIENT FEATURES; 6. MOLECULAR AND BIOLOGICAL MARKERS; 7. THE WAY AHEAD?; REFERENCES; Chapter 2: Survival after Treatment of Intraocular Melanoma; 1. INTRODUCTION |
2. INTRAOCULAR MELANOMA3. STATISTICAL METHODS FOR PREDICTING METASTATIC DISEASE; 4. PREDICTING METASTATIC DEATH WITH NEURAL NETWORKS; 5. MISCELLANEOUS ERRORS; 6. A NEURAL NETWORK FOR PREDICTING SURVIVAL IN UVEAL MELANOMA PATIENTS; 7. CAVEATS REGARDING INTERPRETATION OF SURVIVAL STATISTICS; 8. FURTHER STUDIES; 9. CONCLUSIONS; REFERENCES; Chapter 3: Recent |
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Developments in Relative Survival Analysis; 1. INTRODUCTION; 2. CAUSE-SPECIFIC SURVIVAL; 3. INDEPENDENCE ASSUMPTION; 4. EXPECTED SURVIVAL; 5. RELATIVE SURVIVAL; 6. POINT OF CURE; 7. REGRESSION ANALYSIS; 8. PERIOD ANALYSIS |
9. AGE STANDARDIZATION10. PARAMETRIC METHODS; 11. MULTIPLE TUMOURS; 12. CONCLUSION; REFERENCES; Section 2: Biological and Genetic Factors; Chapter 4: Environmental and Genetic Risk Factors of Lung Cancer; 1. INTRODUCTION; 2. LUNG CANCER INCIDENCE AND MORTALITY; 3. CONCLUSION; REFERENCES; Chapter 5: Chaos, Cancer, the Cellular Operating System and the Prediction of Survival in Head and Neck Cancer; 1. INTRODUCTION; 2. CANCER AND ITS CAUSATION; 3. FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY; 4. A NEW DIRECTION FOR FUNDAMENTAL CELL BIOLOGY AND ONCOLOGY |
5. COMPLEX SYSTEMS ANALYSIS AS APPLIED TO BIOLOGICAL SYSTEMS AND SURVIVAL ANALYSIS6. METHODS OF ANALYSING FAILURE IN BIOLOGICAL SYSTEMS; 7. A COMPARISON OF A NEURAL NETWORK WITH COX'S REGRESSION IN PREDICTING SURVIVAL IN OVER 800 PATIENTS; 8. THE NEURAL NETWORK AND FUNDAMENTAL BIOLOGY AND ONCOLOGY; 9. THE DIRECTION OF FUTURE WORK; 10. SUMMARY; REFERENCES; Section 3: Mathematical Background of Prognostic Models; Chapter 6: Flexible Hazard Modelling for Outcome Prediction in Cancer: Perspectives for the Use of Bioinformatics Knowledge; 1. INTRODUCTION; 2. FAILURE TIME DATA |
3. PARTITION AND GROUPING OF FAILURE TIMES4. COMPETING RISKS; 5. GLMs AND FFANNs; 6. APPLICATIONS TO CANCER DATA; 7. CONCLUSIONS; REFERENCES; Chapter 7: Information Geometry for Survival Analysis and Feature Selection by Neural Networks; 1. INTRODUCTION; 2. SURVIVAL FUNCTIONS; 3. STANDARD MODELS FOR SURVIVAL ANALYSIS; 4. THE NEURAL NETWORK MODEL; 5. LEARNING IN THE CPENN MODEL; 6. THE BAYESIAN APPROACH TO MODELLING; 7. VARIABLE SELECTION; 8. THE LAYERED PROJECTION ALGORITHM; 9. A SEARCH STRATEGY; 10. EXPERIMENTS; 11. CONCLUSION; REFERENCES |
Chapter 8: Artificial Neural Networks Used in the Survival Analysis of Breast Cancer Patients: A Node-Negative Study |
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Sommario/riassunto |
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This book is organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. The first section describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. Amongst issues discussed in this section are the TNM staging, accepted methods for survival analysis and competing risks. The second section describes the biological and genetic markers and the rôle of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effectiv |
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4. |
Record Nr. |
UNINA9910830023503321 |
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Autore |
Jensen Greg <1973-> |
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Titolo |
Spread trading [[electronic resource] ] : an introduction to trading options in nine simple steps / / Greg Jensen |
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Pubbl/distr/stampa |
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Hoboken, NJ, : Wiley, c2009 |
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ISBN |
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1-119-19844-5 |
0-470-48617-1 |
1-282-11455-7 |
9786612114557 |
0-470-48616-3 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (355 p.) |
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Collana |
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Disciplina |
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332.63 |
332.63/2283 |
332.632283 |
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Soggetti |
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Stock options |
Options (Finance) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Spread Trading: An Introduction to Trading Options in Nine Simple Steps; Contents; Acknowledgments; About the Author; Introduction; Step 1: Understanding the Long and Short of "Call" Options in the Market; Step 2: Understanding the Long and Short of "Put" Options in the Market; Step 3: Ramping Up the Possibilities; Step 4: Getting a Few Basics in Place; Step 5: Understanding Two Basic Option Trades; Step 6: Moving from Option Trading to Spread Trading; Step 7: Understanding Bullish Spread Trades; Step 8: Understanding Bearish Spread Trades; Step 9: Getting Started |
Appendix: Answers to End-of-Chapter ReviewsIndex |
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Sommario/riassunto |
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A proven,easy-to-understandmethod for makingmoney with options ""If you've never invested in the stock market,this is the book for you. If you've been investingfor years . . . this is still the book for you. A fantastic introduction to options.""-Jon ""DOCTOR J"" Najarian, Co-founder, OptionMonster.com Spread trading-the practice of combining |
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optiontrades and adjusting them over time-is being used successfullyby more and more professional traders. In this book, Greg Jensenshows nonprofessionals the tremendous advantages thissafe and profitable method offers. In simple a |
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