Search Results - empirical ((algorithmus OR algorithm) OR algorithms)

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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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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Stairs 2006 : Proceedings of the Third Starting AI Researchers' Symposium

Table of Contents: “…Binarization Algorithms for Approximate Updating in Credal NetsOn Generalizing the AGM Postulates; The Two-Variable Situation Calculus; Base Belief Change and Optimized Recovery; Unsupervised Word Sense Disambiguation Using the WWW; Relational Descriptive Analysis of Gene Expression Data; Solving Fuzzy PERT Using Gradual Real Numbers; Approaches to Efficient Resource-Constrained Project Rescheduling; A Comparison of Web Service Interface Similarity Measures; Finding Alternatives Web Services to Parry Breakdowns; Posters; Smart Ride Seeker Introductory Plan…”
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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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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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Applied Artificial Intelligence : Proceedings of the 7th International FLINS Conference. by Arena, Paolo, Fortuna, Luigi

Table of Contents: “…Environmental/economic dispatch using genetic algorithm and fuzzy number ranking method / G. Zhang [and others]. …”
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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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Reliability in biomechanics by Kharmanda, Ghias, El Hami, Abdelkhalak

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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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Other geographies : the influences of Michael Watts

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Intelligent computational systems : a multi-disciplinary perspective

Table of Contents: “…3.2.1. Genetic Algorithm (GA) -- 3.2.2. Particle Swarm Optimization Algorithm (PSO) -- 3.3. …”
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EEG Signal Processing and Machine Learning. by Sanei, Saeid

Table of Contents: “…3.6 Dynamic Modelling of Neuron Action Potential Threshold -- 3.7 Summary -- References -- Chapter 4 Fundamentals of EEG Signal Processing -- 4.1 Introduction -- 4.2 Nonlinearity of the Medium -- 4.3 Nonstationarity -- 4.4 Signal Segmentation -- 4.5 Signal Transforms and Joint Time-Frequency Analysis -- 4.5.1 Wavelet Transform -- 4.5.1.1 Continuous Wavelet Transform -- 4.5.1.2 Examples of Continuous Wavelets -- 4.5.1.3 Discrete-Time Wavelet Transform -- 4.5.1.4 Multiresolution Analysis -- 4.5.1.5 Wavelet Transform Using Fourier Transform -- 4.5.1.6 Reconstruction -- 4.5.2 Synchro-Squeezed Wavelet Transform -- 4.5.3 Ambiguity Function and the Wigner-Ville Distribution -- 4.6 Empirical Mode Decomposition -- 4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function -- 4.8 Filtering and Denoising -- 4.9 Principal Component Analysis -- 4.9.1 Singular Value Decomposition -- 4.10 Summary -- References -- Chapter 5 EEG Signal Decomposition -- 5.1 Introduction -- 5.2 Singular Spectrum Analysis -- 5.2.1 Decomposition -- 5.2.2 Reconstruction -- 5.3 Multichannel EEG Decomposition -- 5.3.1 Independent Component Analysis -- 5.3.2 Instantaneous BSS -- 5.3.3 Convolutive BSS -- 5.3.3.1 General Applications -- 5.3.3.2 Application of Convolutive BSS to EEG -- 5.4 Sparse Component Analysis -- 5.4.1 Standard Algorithms for Sparse Source Recovery -- 5.4.1.1 Greedy-Based Solution -- 5.4.1.2 Relaxation-Based Solution -- 5.4.2 k-Sparse Mixtures -- 5.5 Nonlinear BSS -- 5.6 Constrained BSS -- 5.7 Application of Constrained BSS -- Example -- 5.8 Multiway EEG Decompositions -- 5.8.1 Tensor Factorization for BSS -- 5.8.2 Solving BSS of Nonstationary Sources Using Tensor Factorization -- 5.9 Tensor Factorization for Underdetermined Source Separation -- 5.10 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain.…”
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The silicon jungle : a novel of deception, power, and Internet intrigue by Baluja, Shumeet, 1971-

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Neural networks in chemical reaction dynamics

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

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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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Biomechanics : optimization, uncertainties and reliability by Kharmanda, Ghias, El Hami, Abdelkhalak

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Pattern recognition : a quality of data perspective by Homenda, Władysław, Pedrycz, Witold, 1953-

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