Advances in Bayesian Networks, Jos? A. G?mez; Serafin Moral; Antonio Salmer?n Cer
Автор: Dawn E. Holmes Название: Innovations in Bayesian Networks ISBN: 3540850651 ISBN-13(EAN): 9783540850656 Издательство: Springer Рейтинг: Цена: 191560.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume the editors have brought together contributions from some of the most prestigious researchers in this field.
Автор: Russell G. Almond; Robert J. Mislevy; Linda S. Ste Название: Bayesian Networks in Educational Assessment ISBN: 1493938282 ISBN-13(EAN): 9781493938285 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Introduction.- An Introduction to Evidence-Centered Design.- Bayesian Probability and Statistics: a review.- Basic graph theory and graphical models.- Efficient calculations.- Some Example Networks.- Explanation and Test Construction.- Parameters for Bayesian Network Models.- Learning in Models with Fixed Structure.- Critiquing and Learning Model Structure.- An Illustrative Example.- The Conceptual Assessment Framework.- The Evidence Accumulation Process.- The Biomass Measurement Model.- The Future of Bayesian Networks in Educational Assessment.- Bayesian Network Resources.- References.
Автор: Chengan Guo; Zeng-Guang Hou; Zhigang Zeng Название: Advances in Neural Networks- ISNN 2013 ISBN: 3642390641 ISBN-13(EAN): 9783642390647 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Computational Neuroscience and Cognitive Science.- Information Transfer Characteristic in Memristic Neuromorphic Network.- Generation and Analysis of 3D Virtual Neurons Using Genetic Regulatory Network Model.- A Finite-Time Convergent Recurrent Neural Network Based Algorithm for the L Smallest k-Subsets Sum Problem.- Spike Train Pattern and Firing Synchronization in a Model of the Olfactory Mitral Cell.- Efficiency Improvements for Fuzzy Associative Memory.- A Study of Neural Mechanism in Emotion Regulation by Simultaneous Recording of EEG and fMRI Based on ICA.- Emotion Cognitive Reappraisal Research Based on Simultaneous Recording of EEG and BOLD Responses.- Convergence of Chaos Injection-Based Batch Backpropagation Algorithm For Feedforward Neural Networks.- Discovering the Multi-neuronal Firing Patterns Based on a New Binless Spike Trains Measure.- A Study on Dynamic Characteristics of the Hippocampal Two-Dimension Reduced Neuron Model under Current Conductance Changes.- Neural Network Models, Learning Algorithms, Stability and Convergence Analysis.- Overcoming the Local-Minimum Problem in Training Multilayer Perceptrons with the NRAE-MSE Training Method.- Generalized Single-Hidden Layer Feedforward Networks.- An Approach for Designing Neural Cryptography.- Bifurcation of a Discrete-Time Cohen-Grossberg-Type BAM Neural Network with Delays.- Stability Criteria for Uncertain Linear Systems with Time-Varying Delay.- Generalized Function Projective Lag Synchronization between Two Different Neural Networks.- Application of Local Activity Theory of CNN to the Coupled Autocatalator Model.- Passivity Criterion of Stochastic T-S Fuzzy Systems with Time-Varying Delays.- Parallel Computation of a New Data Driven Algorithm for Training Neural Networks.- Stability Analysis of a Class of High Order Fuzzy Cohen-Grossberg Neural Networks with Mixed Delays and Reaction-Diffusion Terms.- A Study on the Randomness Reduction Effect of Extreme Learning Machine with Ridge Regression.- Stability of Nonnegative Periodic Solutions of High-Ordered Neural Networks.- Existence of Periodic Solution for Competitive Neural Networks with Time-Varying and Distributed Delays on Time Scales.- Global Exponential Stability in the Mean Square of Stochastic Cohen-Grossberg Neural Networks with Time-Varying and Continuous Distributed Delays.- A Delay-Partitioning Approach to Stability Analysis of Discrete-Time Recurrent Neural Networks with Randomly Occurred Nonlinearities.- The Universal Approximation Capabilities of Mellin Approximate Identity Neural Networks.- H∞ Filtering of Markovian Jumping Neural Networks with Time Delays.- Convergence Analysis for Feng's MCA Neural Network Learning Algorithm.- Anti-periodic Solutions for Cohen-Grossberg Neural Networks with Varying-Time Delays and Impulses.- Global Robust Exponential Stability in Lagrange Sense for Interval Delayed Neural Networks.- The Binary Output Units of Neural Network.- Kernel Methods, Large Margin Methods and SVM.- Support Vector Machine with Customized Kernel.- Semi-supervised Kernel Minimum Squared Error Based on Manifold Structure.- Noise Effects on Spatial Pattern Data Classification Using Wavelet Kernel PCA: A Monte Carlo Simulation Study.- SVM-SVDD: A New Method to Solve Data Description Problem with Negative Examples.- Applying Wavelet Packet Decomposition and One-Class Support Vector Machine on Vehicle Acceleration Traces for Road Anomaly Detection.- Aeroengine Turbine Exhaust Gas Temperature Prediction Using Process Support Vector Machines.- The Effect of Lateral Inhibitory Connections in Spatial Architecture Neural Network.- Empirical Mode Decomposition Based LSSVM for Ship Motion Prediction.- Optimization Algorithms / Variational Methods.- Optimal Calculation of Tensor Learning Approaches.- Repeatable Optimization Algorithm Based Discrete PSO for Virtual Network Embedding.- An Energy-Efficient Coverage Optimization Method for the Wireless Sensor Networks Based on
Автор: Dawn E. Holmes Название: Innovations in Bayesian Networks ISBN: 3642098754 ISBN-13(EAN): 9783642098758 Издательство: Springer Рейтинг: Цена: 174130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume the editors have brought together contributions from some of the most prestigious researchers in this field.
Автор: James Kwok; Liqing Zhang; Bao-Liang Lu Название: Advances in Neural Networks -- ISNN 2010 ISBN: 3642133177 ISBN-13(EAN): 9783642133176 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Constitutes the proceedings of the 7th International Symposium on Neural Networks, ISNN 2010, held in Shanghai, China, June 6-9, 2010. This title presents the papers that focus on topics such as SVM and Kernel Methods, Vision and Image, Data Mining and Text Analysis, BCI and Brain Imaging and its applications.
Автор: Thompson John Название: Bayesian Analysis with Stata ISBN: 1597181412 ISBN-13(EAN): 9781597181419 Издательство: Taylor&Francis Рейтинг: Цена: 57150.00 T Наличие на складе: Невозможна поставка. Описание:
Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.
The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.
The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
Автор: Darwiche Название: Modeling and Reasoning with Bayesian Networks ISBN: 1107678420 ISBN-13(EAN): 9781107678422 Издательство: Cambridge Academ Рейтинг: Цена: 65470.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.
Автор: James Kwok; Bao-Liang Lu; Liqing Zhang; Bao-Liang Название: Advances in Neural Networks -- ISNN 2010 ISBN: 3642132774 ISBN-13(EAN): 9783642132773 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Constitutes the proceedings of the 7th International Symposium on Neural Networks, ISNN 2010, held in Shanghai, China, June 6-9, 2010. This title presents the papers that focus on topics such as Neurophysiological Foundation, Theory and Models, Learning and Inference, and Neurodynamics.
Автор: Nagarajan, Radhakrishnan Scutari, Marco Lebre, Sophie Название: Bayesian networks in r ISBN: 1461464455 ISBN-13(EAN): 9781461464457 Издательство: Springer Рейтинг: Цена: 40050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces readers essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. Each chapter includes exercises with solutions.
Автор: Kjarulff, Uffe B. Madsen, Anders L. Название: Bayesian networks and influence diagrams: a guide to construction and analysis ISBN: 1461451035 ISBN-13(EAN): 9781461451037 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In a Second Edition offering six new sections, new examples, tables, figures and more, this book shows how to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Includes more than 140 examples.
Автор: Fengyu Cong; Andrew Leung; Qinglai Wei Название: Advances in Neural Networks - ISNN 2017 ISBN: 3319590715 ISBN-13(EAN): 9783319590714 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 14th International Symposium on Neural Networks, ISNN 2017, held in Sapporo, Hakodate, and Muroran, Hokkaido, Japan, in June 2017. The 135 revised full papers presented in this two-volume set were carefully reviewed and selected from 259 submissions.
Автор: Fengyu Cong; Andrew Leung; Qinglai Wei Название: Advances in Neural Networks - ISNN 2017 ISBN: 3319590804 ISBN-13(EAN): 9783319590806 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 14th International Symposium on Neural Networks, ISNN 2017, held in Sapporo, Hakodate, and Muroran, Hokkaido, Japan, in June 2017. The 135 revised full papers presented in this two-volume set were carefully reviewed and selected from 259 submissions.
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