Search Results - empirical (algorithmics OR algorithms)

Computational materials discovery

Table of Contents: “…3.1.2 Empirical Interatomic Potentials3.1.3 Machine Learning Interatomic Potentials; 3.2 Simple Problem: Fitting of Potential Energy Surfaces; 3.2.1 Representation of Atomic Systems; 3.2.2 An Overview of Machine Learning Methods; 3.3 Machine Learning Interatomic Potentials; 3.3.1 Representation of Atomic Environments; 3.3.2 Existing MLIPs; 3.4 Fitting and Testing of Interatomic Potentials; 3.4.1 Optimization Algorithms; 3.4.2 Validation and Cross-validation; 3.4.3 Learning on the Fly; 3.5 Discussion; 3.5.1 Which Potential Is Better?…”
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Reliability in biomechanics by Kharmanda, Ghias, El Hami, Abdelkhalak

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Other geographies : the influences of Michael Watts

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The silicon jungle : a novel of deception, power, and Internet intrigue by Baluja, Shumeet, 1971-

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Progress in financial markets research

Table of Contents: “…Optimization of Technical Rules by Genetic Algorithms -- 8.5. Empirical Results -- Conclusion -- References -- Chapter 9: MODERN ANALYSIS OF FLUCTUATIONSIN FINANCIAL TIME SERIES AND BEYOND -- 9.1. …”
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Computer-Aided Learning and Analysis for COVID-19 Disease. by Dhiman, Gaurav

Table of Contents: “…Cover -- Special issue (part 1) on computer-aided learning and analysis for COVID-19 disease -- COVID-19: risk prediction through nature inspired algorithm -- E-biomedical: a positive prospect to monitor human healthcare system using blockchain technology -- Pattern analysis: predicting COVID-19 pandemic in India using AutoML -- Predicting future diseases based on existing health status using link prediction -- Detection of COVID-19 cases through X-ray images using hybrid deep neural network -- Time series analysis of COVID-19 cases…”
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Discourse, of course : an overview of research in discourse studies

Table of Contents: “…/ Elisabeth Le -- Implicit and explicit coherence relations / Maite Taboada -- Style and culture in quantitative discourse analysis / Martin Kaltenbacher -- Devices of probability and obligation in text types / Xinzhang Yang -- Analysis and evaluation of argumentative discourse / Frans H. van Eemeren and Bart Garssen -- Embodied cognition, discourse, and dual coding theory : new directions / Mark Sadoski -- The cognition of discourse coherence / Ted Sanders and Wilbert Spooren -- A computational psycholinguistic algorithm to measure cohesion in discourse / Max M. …”
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Retail futures : the good, the bad and the ugly of the digital transformation

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Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models. by Holdaway, Keith R.

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Missing Data Methods : Cross-Sectional Methods and Applications. by Drukker, David M.

Table of Contents: “…NotesAcknowledgment; References; Orthogonality in the single index model; On the estimation of selection models when participation is endogenous and misclassified; Introduction; The model and estimator; Sampling algorithm; Simulated data example; Summary and conclusions; Notes; Acknowledgments; References; summary tables for additional simulations; Process for simulating non -- normal errors; Efficient probit estimation with partially missing covariates; Introduction; Model Specification; Efficient estimators and variances; Testing assumptions and possible modifications; Other models.…”
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Pattern recognition in industry by Bhagat, Phiroz

Table of Contents: “…Preface -- -- Acknowledgments -- About the Author -- <CENTER>Part I Philosophy</CENTER> -- -- CHAPTER 1: INTRODUCTION -- CHAPTER 2: PATTERNS WITHIN DATA -- CHAPTER 3: ADAPTING BIOLOGICAL PRINCIPLES FOR DEPLOYMENT IN COMPUTATIONAL SCIENCE -- CHAPTER 4: ISSUES IN PREDICTIVE EMPIRICAL MODELING -- <CENTER>Part II Technology</CENTER> -- CHAPTER 5: SUPERVISED LEARNINGCORRELATIVE NEURAL NETS -- CHAPTER 6: UNSUPERVISED LEARNING: AUTO-CLUSTERING AND SELF-ORGANIZING DATA -- CHAPTER 7: CUSTOMIZING FOR INDUSTRIAL STRENGTH APPLICATIONS -- CHAPTER 8: CHARACTERIZING AND CLASSIFYING TEXTUAL MATERIAL -- CHAPTER 9: PATTERN RECOGNITION IN TIME SERIES ANALYSIS -- CHAPTER 10: GENETIC ALGORITHMS -- <CENTER>Part III Case Studies</CENTER> -- CHAPTER 11: HARNESSING THE TECHNOLOGY FOR PROFITABILITY -- CHAPTER 12: REACTOR MODELING THROUGH IN SITU ADAPTIVE LEARNING -- CHAPTER 13: PREDICTING PLANT STACK EMISSIONS TO MEET ENVIRONMENTAL LIMITS -- CHAPTER 14: PREDICTING FOULING/COKING IN FIRED HEATERS -- CHAPTER 15: PREDICTING OPERATIONAL CREDITS -- CHAPTER 16: PILOT PLANT SCALE-UP BY INTERPRETING TRACER DIAGNOSTICS -- CHAPTER 17: PREDICTING DISTILLATION TOWER TEMPERATURES: MINING DATA FOR CAPTURING DISTINCT OPERATIONAL VARIABILITY -- CHAPTER 18: ENABLING NEW PROCESS DESIGN BASED ON LABORATORY DATA -- CHAPTER 19: FORECASTING PRICE CHANGES OF A COMPOSITE BASKET OF COMMODITIES -- CHAPTER 20: CORPORATE DEMOGRAPHIC TREND ANALYSIS -- EPILOGUE -- <CENTER>Appendices</CENTER> -- APPENDIX A1: THERMODYNAMICS AND INFORMATION THEORY -- APPENDIX A2: MODELING.…”
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Pattern recognition : a quality of data perspective by Homenda, Władysław, Pedrycz, Witold, 1953-

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Mechanisms and games for dynamic spectrum allocation

Table of Contents: “…7.3.10 Other equilibrium concepts -- 7.4 Learning equilibria -- 7.4.1 Learning Nash equilibria -- 7.4.2 Learning epsilon-equilibrium -- 7.4.3 Learning coarse correlated equilibrium -- 7.4.4 Learning satisfaction equilibrium -- 7.4.5 Discussion -- 7.5 Conclusion -- References -- II Cognitive radio and sharing of unlicensed spectrum -- 8 Cooperation in cognitiveradio networks: from accessto monitoring -- 8.1 Introduction -- 8.1.1 Cooperation in cognitive radio: mutual benefits and costs -- 8.2 An overview of coalitional game theory -- 8.3 Cooperative spectrum exploration and exploitation -- 8.3.1 Motivation -- 8.3.2 Basic problem -- 8.3.3 Joint sensing and access as a cooperative game -- 8.3.4 Coalition formation algorithm for joint sensing and access -- 8.3.5 Numerical results -- 8.4 Cooperative primary user activity monitoring -- 8.4.1 Motivation -- 8.4.2 Primary user activity monitoring: basic model -- 8.4.3 Cooperative primary user monitoring -- 8.4.4 Numerical results -- 8.5 Summary -- Acknowledgements -- Copyright notice -- References -- 9 Cooperative cognitive radios with diffusion networks -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Basic tools in convex and matrix analysis -- 9.2.2 Graphs -- 9.3 Distributed spectrum sensing -- 9.4 Iterative consensus-based approaches -- 9.4.1 Average consensus algorithms -- 9.4.2 Acceleration techniques for iterative consensus algorithms -- 9.4.3 Empirical evaluation -- 9.5 Consensus techniques based on CoMAC -- 9.6 Adaptive distributed spectrum sensing based on adaptive subgradient techniques -- 9.6.1 Distributed detection with adaptive filters -- 9.6.2 Set-theoretic adaptive filters for distributed detection -- 9.6.3 Empirical evaluation -- 9.7 Channel probing -- 9.7.1 Introduction -- 9.7.2 Admissibility problem -- 9.7.3 Power and admission control algorithms.…”
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Big data and differential privacy : analysis strategies for railway track engineering by Attoh-Okine, Nii O.

Table of Contents: “…3.12.2.2 Deep Belief Nets (DBN)3.12.3 Convolutional Neural Networks (CNN); 3.12.4 Granular Computing (Rough Set Theory); 3.12.5 Clustering; 3.12.5.1 Measures of Similarity or Dissimilarity; 3.12.5.2 Hierarchical Methods; 3.12.5.3 Non-Hierarchical Clustering; 3.12.5.4 k-Means Algorithm; 3.12.5.5 Expectation-Maximization (EM) Algorithms; 3.13 Data Stream Processing; 3.13.1 Methods and Analysis; 3.13.2 LogLog Counting; 3.13.3 Count-Min Sketch; 3.13.3.1 Online Support Regression; 3.14 Remarks; References; Chapter 4 Basic Foundations of Big Data; 4.1 Introduction; 4.2 Query.…”
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Genomic Signal Processing. by Shmulevich, Ilya

Table of Contents: “…5.2 Complexity Regularization5.2.1 Regularization of the Error; 5.2.2 Structural Risk Minimization; 5.2.3 Empirical Complexity ; 5.3 Feature Selection; 5.3.1 Peaking Phenomenon; 5.3.2 Feature Selection Algorithms; 5.3.3 Impact of Error Estimation on Feature Selection; 5.3.4 Redundancy; 5.3.5 Parallel Incremental Feature Selection; 5.3.6 Bayesian Variable Selection; 5.4 Feature Extraction; Bibliography; 6 Clustering; 6.1 Examples of Clustering Algorithms; 6.1.1 Euclidean Distance Clustering; 6.1.2 Self-Organizing Maps; 6.1.3 Hierarchical Clustering; 6.1.4 Model-Based Cluster Operators.…”
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A Case for the Existence of God. by Overman, Dean L.

Table of Contents: “…Chapter 11: Recorded Experiences of Encounters with the Divine Bear Witness to a Way of Knowing that Includes Kierkegaard's KENDSKAB, BUBER'S I-Thou, OTTO'S Wholly Other, AND MARCEL'S MysteryChapter 12: THESE NINE WITNESSES TESTIFY TO ANOTHER WAY OF KNOWING THAT IS COMPATIBLE WITH THE EMPIRICAL AND THE METAPHYSICAL RATIONAL WAYS OF KNOWING, BUT IS BEYOND THE DESCRIBABLE AND REQUIRES PERSO; Chapter 13: CONCLUDING REFLECTIONS AND SUMMARY; AFTERWORD; Appendix A: THE NEW MATHEMATICS OF ALGORITHMIC INFORMATION THEORY IS RELEVANT TO THEORIES CONCERNING THE FORMATION OF THE FIRST LIVING MATTER.…”
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Qualitative Comparative Analysis : An Introduction To Research Design And Application. by Mello, Patrick A.

Table of Contents: “…Related Methods and Approaches -- Notes -- 9 QCA and Its Critics -- Analytical Robustness -- Comparisons with Other Methods -- Formalization and Algorithms -- Causal Analysis and Solution Terms -- Recognizing QCA's Strengths and Limitations -- Notes -- 10 Guiding Principles for QCA Research -- Good Research Practice -- Documenting and Communicating QCA Results -- QCA Resources -- Current Developments -- The Way Ahead -- Notes -- Appendix: Link to the Online R Manual -- Glossary -- A -- B -- C -- D -- E -- F -- G -- I -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- X-Y -- References…”
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Biomechanics : optimization, uncertainties and reliability by Kharmanda, Ghias, El Hami, Abdelkhalak

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Linkage Analysis and Gene Mapping. by WANG, Jiankang

Table of Contents: “…Theoretical Frequencies of 4 Homozygotes in Permanent Populations -- Genotypic Frequencies of Two Co-Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Dominant Locus in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Recessive Loci in Temporary Populations -- Estimation of Two-Point Recombination Frequency -- Maximum Likelihood Estimation of Recombination Frequency in DH Populations -- General Procedure on the Maximum Likelihood Estimation of Recombination Frequency -- Estimation of Recombination Frequency Between One Co-Dominant and One Dominant Marker in F2 Population -- Initial Values in Newton Algorithm -- EM Algorithm in Estimating Recombination Frequency in F2 Populations -- Effects on the Estimation of Recombination Frequency from Segregation Distortion -- Exercises -- Three-Point Analysis and Linkage Map Construction -- Three-Point Analysis and Mapping Function -- Genetic Interference and Coefficient of Interference -- Mapping Function -- Construction of Genetic Linkage Maps -- Marker Grouping Algorithm -- Marker Ordering Algorithm -- Use of the k-Optimal Algorithm in Linkage Map Construction -- Rippling of the Ordered Markers -- Integration of Multiple Maps -- Comparison of the Recombination Frequency Estimation in Different Populations -- LOD Score in Testing the Linkage Relationship in Different Populations -- Accuracy of the Estimated Recombination Frequency -- Least Population Size to Declare the Significant Linkage Relationship and Close Linkage -- Linkage Analysis in Random Mating Populations.…”
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Social-behavioral modeling for complex systems

Table of Contents: “…Cover; Title Page; Copyright; Contents; Foreword; List of Contributors; About the Editors; About the Companion Website; Part I Introduction and Agenda; Chapter 1 Understanding and Improving the Human Condition: A Vision of the Future for Social-Behavioral Modeling; Challenges; Challenge One: The Complexity of Human Issues; Challenge Two: Fragmentation; Empirical Observation; Empirical Experiments; Generative Simulation; Unification; Challenge Three: Representations; Challenge Four: Applications of Social-Behavioral Modeling; About This Book; Roadmap for the Book; References…”
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