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

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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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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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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Mathematics of evolution and phylogeny

Table of Contents: “…List of Contributors -- 1 The minimum evolution distance-based approach of phylogenetic inference -- 1.1 Introduction -- 1.2 Tree metrics -- 1.2.1 Notation and basics -- 1.2.2 Three-point and four-point conditions -- 1.2.3 Linear decomposition into split metrics -- 1.2.4 Topological matrices -- 1.2.5 Unweighted and balanced averages -- 1.2.6 Alternate balanced basis for tree metrics -- 1.2.7 Tree metric inference in phylogenetics -- 1.3 Edge and tree length estimation -- 1.3.1 The LS approach -- 1.3.2 Edge length formulae -- 1.3.3 Tree length formulae -- 1.3.4 The positivity constraint -- 1.3.5 The balanced scheme of Pauplin -- 1.3.6 Semple and Steel combinatorial interpretation -- 1.3.7 BME: a WLS interpretation -- 1.4 The agglomerative approach -- 1.4.1 UPGMA and WPGMA -- 1.4.2 NJ as a balanced minimum evolution algorithm -- 1.4.3 Other agglomerative algorithms -- 1.5 Iterative topology searching and tree building -- 1.5.1 Topology transformations.; 1.5.2 A fast algorithm for NNIs with OLS -- 1.5.3 A fast algorithm for NNIs with BME -- 1.5.4 Iterative tree building with OLS -- 1.5.5 From OLS to BME -- 1.6 Statistical consistency -- 1.6.1 Positive results -- 1.6.2 Negative results -- 1.6.3 Atteson's safety radius analysis -- 1.7 Discussion -- Acknowledgements -- 2 Likelihood calculation in molecular phylogenetics -- 2.1 Introduction -- 2.2 Markov models of sequence evolution -- 2.2.1 Independence of sites -- 2.2.2 Setting up the basic model -- 2.2.3 Stationary distribution -- 2.2.4 Time reversibility -- 2.2.5 Rate of mutation -- 2.2.6 Probability of sequence evolution on a tree -- 2.3 Likelihood calculation: the basic algorithm -- 2.4 Likelihood calculation: improved models -- 2.4.1 Choosing the rate matrix -- 2.4.2 Among site rate variation -- 2.4.3 Site-specific rate variation -- 2.4.4 Correlated evolution between sites -- 2.5 Optimizing parameters -- 2.5.1 Optimizing continuous parameters -- 2.5.2 Searching for the optimal tree.; 2.5.3 Alternative search strategies -- 2.6 Consistency of the likelihood approach -- 2.6.1 Statistical consistency -- 2.6.2 Identifiability of the phylogenetic models -- 2.6.3 Coping with errors in the model -- 2.7 Likelihood ratio tests -- 2.7.1 When to use the asymptotic x2 distribution -- 2.7.2 Testing a subset of real parameters -- 2.7.3 Testing parameters with boundary conditions -- 2.7.4 Testing trees -- 2.8 Concluding remarks -- Acknowledgements -- 3 Bayesian inference in molecular phylogenetics -- 3.1 The likelihood function and maximum likelihood estimates -- 3.2 The Bayesian paradigm -- 3.3 Prior -- 3.4 Markov chain Monte Carlo -- 3.4.1 Metropolis-Hastings algorithm -- 3.4.2 Single-component Metropolis-Hastings algorithm -- 3.4.3 Gibbs sampler -- 3.4.4 Metropolis-coupled MCMC -- 3.5 Simple moves and their proposal ratios -- 3.5.1 Sliding window using uniform proposal -- 3.5.2 Sliding window using normally distributed proposal.; 3.5.3 Sliding window using normal proposal in multidimensions -- 3.5.4 Proportional shrinking and expanding -- 3.6 Monitoring Markov chains and processing output -- 3.6.1 Diagnosing and validating MCMC algorithms -- 3.6.2 Gelman and Rubin's potential scale reduction statistic -- 3.6.3 Processing output -- 3.7 Applications to molecular phylogenetics -- 3.7.1 Estimation of phylogenies -- 3.7.2 Estimation of species divergence times -- 3.8 Conclusions and perspectives -- Acknowledgements -- 4 Statistical approach to tests involving phylogenies -- 4.1 The statistical approach to phylogenetic inference -- 4.2 Hypotheses testing -- 4.2.1 Null and alternative hypotheses -- 4.2.2 Test statistics -- 4.2.3 Significance and power -- 4.2.4 Bayesian hypothesis testing -- 4.2.5 Questions posed as function of the tree parameter -- 4.2.6 Topology of treespace -- 4.2.7 The data -- 4.2.8 Statistical paradigms -- 4.2.9 Distributions on treespace -- 4.3 Different types of tests involving phylogenies.; 4.3.1 Testing t1 versus t2 -- 4.3.2 Conditional tests -- 4.3.3 Modern Bayesian hypothesis testing -- 4.3.4 Bootstrap tests -- 4.4 Non-parametric multivariate hypothesis testing -- 4.4.1 Multivariate con.dence regions -- 4.5 Conclusions: there are many open problems -- Acknowledgements -- 5 Mixture models in phylogenetic inference -- 5.1 Introduction: models of gene-sequence evolution -- 5.2 Mixture models -- 5.3 Defining mixture models -- 5.3.1 Partitioning and mixture models -- 5.3.2 Discrete-gamma model as a mixture model -- 5.3.3 Combining rate and pattern-heterogeneity -- 5.4 Digression: Bayesian phylogenetic inference -- 5.4.1 Bayesian inference of trees via MCMC -- 5.5 A mixture model combining rate and pattern-heterogeneity -- 5.5.1 Selected simulation results -- 5.6 Application of the mixture model to inferring the phylogeny of the mammals -- 5.6.1 Model testing -- 5.7 Results -- 5.7.1 How many rate matrices to include in the mixture model?…”
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The New Politics of Visibility : Spaces, Actors, Practices and Technologies in The Visible. by Brighenti, Andrea Mubi

Table of Contents: “…Three mediated visibility regimes and their differing regimes of recognition -- Broadcast visibility, representational recognition regime -- Networked visibility, enabling (mis)recognition regime -- Algorithmic visibility, paradoxical recognition regime -- Conclusion -- Notes -- References -- 5 The Democratization of Visibility Capital: Face in the Age of Its Automated Technical Reproducibility -- Asymmetry and recognition -- What is visibility capital? …”
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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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Discrete choice experiments in marketing : use of priors in efficient choice designs and their application to individual preference measurement by Zwerina, Klaus, 1967-

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Data Mining and Statistics for Decision Making by Tufféry, Stéphane, Tufféry, Stéphane

Table of Contents: “…Learning algorithms -- 8.7. The main neural networks -- Automatic clustering methods -- 9.1. …”
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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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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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Language learnability and language development by Pinker, Steven, 1954-

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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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Neural control engineering : the emerging intersection between control theory and neuroscience by Schiff, Steven J.

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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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Computational phraseology

Table of Contents: “…Monocollocable words : a type of language combinatory periphery / František Čermák -- Translation asymmetries of multiword expressions in machine translation : an analysis of the TED-MWE corpus / Johanna Monti, Mihael Arcan and Federico Sangati -- German constructional phrasemes and their Russian counterparts : a corpus-based study / Dmitrij Dobrovol'skij -- Computational phraseology and translation studies : from theoretical hypotheses to practical tools / Jean-Pierre Colson -- Computational extraction of formulaic sequences from corpora : two case studies of a new extraction algorithm / Alexander Wahl and Stefan Th. Gries -- Computational phraseology discovery in corpora with the MWETOOLKIT / Carlos Ramisch -- Multiword expressions in comparable corpora / Peter Ďurčo -- Collecting collocations from general and specialised corpora : a comparative analysis / Marie-Claude L'Homme and Daphnée Azoulay -- What matters more : the size of the corpora or their quality? …”
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Performance evaluation and Army recruiting by Dertouzos, James N., 1950-

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Computational materials engineering : achieving high accuracy and efficiency in metals processing simulations by Pietrzyk, Maciej, 1947-, Madej, Łukasz, Rauch, Lukasz, Szeliga, Danuta

Table of Contents: “…Toward Increase of the Efficiency of Modeling -- 2.1 Improvement of Numerical Algorithms -- 2.1.1 Metamodeling -- 2.1.2 Inverse analysis -- 2.1.2.1 General formulation of the inverse problem -- 2.1.2.2 Regularization -- 2.1.2.3 Methods of regularizations -- 2.1.2.4 Numerical computations and regularization in the finite-dimension setting -- 2.1.3 Sensitivity analysis -- 2.1.3.1 Local SA -- 2.1.3.2 Global SA -- 2.1.3.3 The implementation of SA algorithms -- 2.1.3.4 A strategy for the identification of the model parameters -- 2.2 Improvement of Hardware -- 2.2.1 General idea of high-performance computing -- 2.2.2 Development of clusters -- 2.2.3 Development of heterogeneous architectures -- 2.2.4 Development of grid environments -- 3. …”
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Handbook of modeling high-frequency data in finance

Table of Contents: “…Handbook of Modeling High-Frequency Data in Finance; Contents; Preface; Contributors; Part One Analysis of Empirical Data; 1 Estimation of NIG and VG Models for High Frequency Financial Data; 1.1 Introduction; 1.2 The Statistical Models; 1.3 Parametric Estimation Methods; 1.4 Finite-Sample Performance via Simulations; 1.5 Empirical Results; 1.6 Conclusion; References; 2 A Study of Persistence of Price Movement using High Frequency Financial Data; 2.1 Introduction; 2.2 Methodology; 2.3 Results; 2.4 Rare Events Distribution; 2.5 Conclusions; References…”
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Reviews in computational chemistry.

Table of Contents: “…Small Molecule Docking and Scoring; Introduction; Algorithms for Molecular Docking; The Docking Problem; Placing Fragments and Rigid Molecules; Flexible Ligand Docking; Handling Protein Flexibility; Docking of Combinatorial Libraries; Scoring; Shape and Chemical Complementary Scores; Force Field Scoring; Empirical Scoring Functions; Knowledge-Based Scoring Functions; Comparing Scoring Functions in Docking Experiments: Consensus Scoring.…”
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Applications of GRA and grey prediction models

Table of Contents: “…Combined forecasting of regional logistics demand optimized by genetic algorithmApplication of grey relational analysis to expose individual student's cognitive difficulties in English public speaking; Mobile communication service income prediction method based on grey buffer operator theory; Using grey relational analysis to evaluate resource configuration and service ability for hospital on public private partnership model in China; The status of traditional medicine and national medicine in different areas of the China in 2011 with grey clustering analysis.…”
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