Chu Kiong Loo; Yap Keem Siah; Kok Wai Wong; Andrew Neural Information Processing 9783319126364

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Автор: Chu Kiong Loo; Yap Keem Siah; Kok Wai Wong; Andrew
Название: Neural Information Processing
ISBN: 9783319126364
Издательство: Springer
Классификация:

Извлечение знаний из данных

Нейронные сети и нечеткие системы

Распознавание образов

Обработка изображений

Разработка и эксплуатационная простота пользовательского интерфейса

ISBN-10: 3319126369
Обложка/Формат: Paperback
Страницы: 635
Вес: 0.92 кг.
Дата издания: 06.11.2014
Серия: Theoretical Computer Science and General Issues
Язык: English
Размер: 234 x 156 x 34
Основная тема: Computer Science
Подзаголовок: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part I
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Cognitive Science.-Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity.- Human Implicit Intent Discrimination Using EEG and Eye Movement.- Towards Establishing Relationships Between Human Arousal level and Motion Mass.- Estimating Nonlinear Spatiotemporal Membrane Dynamics in Active Dendrites.- Inter Subject Correlation of Brain Activity during Visuo-Motor Sequence Learning.- An Agent Response System based on Mirror Neuron and Theory of Mind.- Dynamic of Nitric Oxide Diffusion in Volume Transmission: Model and Validation.- A Computational Model of the Relation Between Regulation of Negative Emotions and Mood.- Neural Networks and Learning Systems - Theory and Design.- Low-cost Representation for Restricted Boltzmann Machines.- Add-if-Silent Rule for Training Multi-layered Convolutional Network Neocognitron.- Posterior Distribution Learning (PDL): A Novel Supervised Learning Framework Including Unlabeled Samples Distribution into Decision.- Computational Model of Neocortical Learning Process: Prototype.- Active Learning with Maximum Density and Minimum Redundancy.- One-to-Many Association Ability of Chaotic Quaternionic Multidirectional Associative Memory.- An Entropy-guided Adaptive Co-construction Method of State and Action Spaces in Reinforcement Learning.- A Nodes Reduction Procedure for RBFNDDA through Histogram.- Toroidal approximate identity neural networks are universal approximators.- Self-Organizing Neural Grove.- Transfer learning using the Online FMM model.- A supervised methodology to measure the variables contribution to a clustering.- Coupling between spatial consistency of neural ring and local eld potential coherence: A computational study.- Fading channel prediction based on self-optimizing neural networks.- Invariant Multiparameter Sensitivity of Oscillator Network.- Spatial-Temporal Saliency Feature Extraction For Robust Mean-Shift Tracker.- BOOSTRON: Boosting Based Perceptron Learning.- G-Stream: Growing Neural Gas over Data Stream.- Combining Active Learning and Semi-Supervised Learning Using Local and Global Consistency.- Complex-Valued Neural Networks -- Recent Progress and Future Directions.- A Cascade System of Simple Dynamic Binary Neural Networks and Its Sparsification.- A Model of V4 Neurons Based on Sparse Coding.- A fast and memory-efficient hierarchical graph clustering algorithm.- Hopfield-Type Associative Memory with Sparse Modular Networks.- Concept Drift Detection based on Anomaly Analysis.- Online learning for faulty RBF networks with the concurrent fault.- The Performance of the Stochastic DNN-kWTA network.- Modularity maximization adjusted by neural networks.- A dynamic pruning strategy for incremental learning on a budget.- Neural Computing with Concurrent Synchrony.- A Line-partitioned Heteroassociative Memory for Storing Binary Fresnel Hologram.- A Unified Framework for Privacy Preserving Data Clustering.- Spiking Neural Network with Lateral Inhibition for Reward-based Associative Learning.- Fuzzy Signature Neural Networks for Classification: Optimising the Structure.- Self-Organizing Map-based Probabilistic Associative Memory.- Neural Networks and Learning Systems - Applications.- A Causal Model for Disease Pathway Discovery.- Enhanced Non-linear Features for On-line Handwriting Recognition Using Deep Learning.- Recognizing Human Actions by Using the Evolving Remote Supervised Method of Spiking Neural Networks.- A Neural Networks Committee for the Contextual Bandit Problem.- Multi-step Predictions of Landslide Displacements based on Echo State Network.- Discrete-Time Nonlinear Generalized Policy Iteration for Optimal Control Using Neural Networks.- ANFIS-Based Model for Improved Paraphrase Rating Prediction.- Contextual Bandit for Active Learning: Active Thompson Sampling.- Choosing the Best Auto-encoder-based Bagging Classifier: An Empirical Study.- Classification of fMRI Data in the NeuCube Evolving Spiking Neural Ne

Neural Information Processing, Chu Kiong Loo; Yap Keem Siah; Kok Wai Wong; Andrew