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Advances in Bayesian Networks, Jos? A. G?mez; Serafin Moral; Antonio Salmer?n Cer


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Автор: Jos? A. G?mez; Serafin Moral; Antonio Salmer?n Cer
Название:  Advances in Bayesian Networks
ISBN: 9783642058851
Издательство: Springer
Классификация:



ISBN-10: 364205885X
Обложка/Формат: Paperback
Страницы: 328
Вес: 0.48 кг.
Дата издания: 15.12.2010
Серия: Studies in Fuzziness and Soft Computing
Язык: English
Размер: 234 x 156 x 18
Основная тема: Mathematics
Ссылка на Издательство: Link
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Поставляется из: Германии


Bayesian networks in r

Автор: Nagarajan, Radhakrishnan Scutari, Marco Lebre, Sophie
Название: Bayesian networks in r
ISBN: 1461464455 ISBN-13(EAN): 9781461464457
Издательство: Springer
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Цена: 6008.00 р.
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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.

Bayesian networks and influence diagrams: a guide to construction and analysis

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
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Цена: 16769.00 р.
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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.

Advances in Neural Networks - ISNN 2017

Автор: Fengyu Cong; Andrew Leung; Qinglai Wei
Название: Advances in Neural Networks - ISNN 2017
ISBN: 3319590715 ISBN-13(EAN): 9783319590714
Издательство: Springer
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Цена: 11179.00 р.
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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.

Advances in Neural Networks - ISNN 2017

Автор: Fengyu Cong; Andrew Leung; Qinglai Wei
Название: Advances in Neural Networks - ISNN 2017
ISBN: 3319590804 ISBN-13(EAN): 9783319590806
Издательство: Springer
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Цена: 11179.00 р.
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.

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

Автор: Uffe B. Kj?rulff; Anders L. Madsen
Название: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
ISBN: 1493900293 ISBN-13(EAN): 9781493900299
Издательство: Springer
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Цена: 18167.00 р.
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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.

Bayesian Networks in Educational Assessment

Автор: Russell G. Almond; Robert J. Mislevy; Linda S. Ste
Название: Bayesian Networks in Educational Assessment
ISBN: 149392124X ISBN-13(EAN): 9781493921249
Издательство: Springer
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Цена: 12577.00 р.
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Описание: Bayesian Networks in Educational Assessment

Advances in Neural Networks – ISNN 2015

Автор: Xiaolin Hu; Yousheng Xia; Yunong Zhang; Dongbin Zh
Название: Advances in Neural Networks – ISNN 2015
ISBN: 3319253921 ISBN-13(EAN): 9783319253923
Издательство: Springer
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Цена: 10062.00 р.
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Описание: The volume LNCS 9377 constitutes the refereed proceedings of the 12th International Symposium on Neural Networks, ISNN 2015, held in Jeju, South Korea in October 2015.

Bayesian Networks in Educational Assessment

Автор: Russell G. Almond; Robert J. Mislevy; Linda S. Ste
Название: Bayesian Networks in Educational Assessment
ISBN: 1493938282 ISBN-13(EAN): 9781493938285
Издательство: Springer
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Цена: 11878.00 р.
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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.

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

Автор: Uffe B. Kj?rulff; Anders L. Madsen
Название: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
ISBN: 1441925465 ISBN-13(EAN): 9781441925466
Издательство: Springer
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Цена: 10754.00 р.
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Описание: This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. The theory and methods presented are illustrated through more than 140 examples.

Switching Networks: Recent Advances

Автор: Ding-Zhu Du; Hung Q. Ngo
Название: Switching Networks: Recent Advances
ISBN: 1461379768 ISBN-13(EAN): 9781461379768
Издательство: Springer
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Цена: 13974.00 р.
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Advances in Neural Networks- ISNN 2013

Автор: Chengan Guo; Zeng-Guang Hou; Zhigang Zeng
Название: Advances in Neural Networks- ISNN 2013
ISBN: 3642390641 ISBN-13(EAN): 9783642390647
Издательство: Springer
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Цена: 6986.00 р.
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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

Innovations in Bayesian Networks

Автор: Dawn E. Holmes
Название: Innovations in Bayesian Networks
ISBN: 3540850651 ISBN-13(EAN): 9783540850656
Издательство: Springer
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Цена: 28734.00 р.
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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.


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