Search Results - empirical (algorithmics OR algorithmic)

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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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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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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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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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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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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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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Essentials of Monte Carlo Simulation : Statistical Methods for Building Simulation Models. by Thomopoulos, Nick T.

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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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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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Oil and gas exploration : methods and application

Table of Contents: “…Theory; 3.2.1. EMD Algorithm; 3.2.2. Gaussian Noise Model and EMD Signal Denoising; 3.2.3. …”
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Harry Markowitz : selected works by Markowitz, Harry M., 1927-2023

Table of Contents: “…Computation of mean-semivariance efficient sets by the critical line algorithm. Data mining corrections -- ch. 7. Harry Markowitz Company. …”
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Empathic space : the computation of human-centric architecture

Table of Contents: “…Exhaustive Enumeration of Small Rectangular PlansEnumeration of Built Forms: An 'Archetypal Building'; An 'Architectural Morphospace'; The Deep Structure of the Picturesque; Form as Process; The Failure of the Top-Down Approach; Thinking Algorithmically; Crafting Space: Generative Processes of Architectural Configurations; Space Block Hanoi, 36 Old Streets district, Hanoi, Vietnam; Solutions You Cannot Draw; Bottom-Up Urban Design; Evolutionary Building Design; Reverse Engineering from the Shadow; Recent Projects with Adaptive Architecture; INTERACTIONS IN THE FIELD.…”
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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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Handbook of high-frequency trading and modeling in finance

Table of Contents: “…4.1 Introduction4.2 Background; 4.2.1 Portfolios And Optimization; 4.2.2 Algorithmic Complexity; 4.2.3 Performance; 4.2.4 Ising Model; 4.2.5 Adiabatic Quantum Computing; 4.3 The models; 4.3.1 Financial Model; 4.3.2 Graph-Theoretic Combinatorial Optimization Models; 4.3.3 Ising And Qubo Models; 4.3.4 Mixed Models; 4.4 Methods; 4.4.1 Model Implementation; 4.4.2 Input Data; 4.4.3 Mean-Variance Calculations; 4.4.4 Implementing The Risk Measure; 4.4.5 Implementation Mapping; 4.5 Results; 4.5.1 The Simple Correlation Model; 4.5.2 The Restricted Minimum-Risk Model.…”
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Current development of mechanical engineering and energy : selected, peer reviewed papers from the 2013 International Symposium on Vehicle, Mechanical, and Electrical Engineering (...

Table of Contents: “…Simulation and Study on Ride Comfort of Articulated Dump Truck Based on Rigid-Flex CouplingThe Hall Automotive Wheel Speed Sensor and Simulation for its Signal Processing; Wind-Induced Vibration of the Rear-View Mirrors of Car at High Speed; Research on the Affection of Vehicle Lateral Stability Related to the Improved ABS; The Quantification Research of Engine Body Defect that Tested by Ultrasonic Phased Array; Analysis of Vehicle Interior Low-Frequency Noise Based on ATV; The Engine Oil Sump Radiated Noise Optimization Design; A Novel OFDM Radar Algorithm in Vehicular Environment…”
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Biomechanics : optimization, uncertainties and reliability by Kharmanda, Ghias, El Hami, Abdelkhalak

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Rationality and decision making : from normative rules to heuristics

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A practitioner's guide to asset allocation by Kinlaw, William, Kinlaw, William, Kritzman, Mark P., Turkington, David, 1983-

Table of Contents: “…-- Linear Hedging Strategies -- Nonlinear Hedging Strategies -- Economic Intuition -- References -- Notes -- Chapter 11: Illiquidity -- The Challenge -- Shadow Assets and Liabilities -- Expected Return and Risk of Shadow Allocations -- Other Considerations -- Case Study -- The Bottom Line -- Appendix -- References -- Notes -- Chapter 12: Risk in the Real World -- The Challenge -- End-of-Horizon Exposure to Loss -- Within-Horizon Exposure to Loss -- Regimes -- The Bottom Line -- References -- Notes -- Chapter 13: Estimation Error -- The Challenge -- Traditional Approaches to Estimation Error -- Stability-Adjusted Optimization -- Building a Stability-Adjusted Return Distribution -- Determining the Optimal Allocation -- Empirical Analysis -- The Bottom Line -- References -- Notes -- Chapter 14: Leverage versus Concentration -- The Challenge -- Leverage in Theory -- Leverage in Practice -- The Bottom Line -- References -- Notes -- Chapter 15: Rebalancing -- The Challenge -- The Dynamic Programming Solution -- The Markowitz-van Dijk Heuristic -- The Bottom Line -- References -- Notes -- Chapter 16: Regime Shifts -- The Challenge -- Predictability of Return and Risk -- Regime-Sensitive Allocation -- Tactical Asset Allocation -- The Bottom Line -- Appendix: Baum-Welch Algorithm -- References -- Notes -- Section Four: Addendum -- Chapter 17: Key Takeaways.…”
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Advances in applied mechanics and materials : selected, peer reviewed papers from the International Conference on Mechanical Engineering (ICOME 2013), September 19-21, 2013, Matara...

Table of Contents: “…Validation of AWTSim as Aerodynamic Analysis for Design Wind Turbine BladeNumerical Study on the Influence of the Corner Curvature of Circular Micropillar on Microdroplet Size via a Dewetting Process; Redesign ITS Central Library through Smart Building; Optimization of Maximum Lift to Drag Ratio on Airfoil Design Based on Artificial Neural Network Utilizing Genetic Algorithm; Carbon Dioxide Effects on the Flammability Characteristics of Biogas; Heat Transfer Effectiveness and Coefficient of Pressure Drop on the Shell Side of a Staggered Elliptical Tubes Bank.…”
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