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Neural Networks is a monthly peer-reviewed scientific journal and an official journal of the International Neural Network Society, European Neural Network Society, and Japanese Neural Network Society. Source: Wikipedia (en)
Editions published in Neural Networks 200
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Learning contextualized semantics from co-occurring terms via a Siamese architecture.
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Robust mixture of experts modeling using the t distribution
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Two fast and accurate heuristic RBF learning rules for data classification
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A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data
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Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels
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Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification
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Attribute-based Decision Graphs: A framework for multiclass data classification
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Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells
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Function approximation in inhibitory networks
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Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays
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Discontinuous Lyapunov approach to state estimation and filtering of jumped systems with sampled-data
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Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM.
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Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.
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A new robust model of one-class classification by interval-valued training data using the triangular kernel.
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Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs
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Maximum margin semi-supervised learning with irrelevant data
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A decentralized training algorithm for Echo State Networks in distributed big data applications.
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Smart sampling and incremental function learning for very large high dimensional data.
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Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications.
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Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by data-sampling
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Neuro-genetic system for optimization of GMI samples sensitivity
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NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
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Similarity preserving low-rank representation for enhanced data representation and effective subspace learning
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Detecting cells using non-negative matrix factorization on calcium imaging data
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Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding
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Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms.
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Corrigendum to “The unimodal model for the classification of ordinal data” [Neural Netw. 21 (2008) 78–79].
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Towards an intelligent framework for multimodal affective data analysis
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Change-point detection in time-series data by relative density-ratio estimation
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A neural network algorithm for semi-supervised node label learning from unbalanced data
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An improved analysis of the Rademacher data-dependent bound using its self bounding property
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Stochastic sampled-data control for state estimation of time-varying delayed neural networks
Subject - wd:Q15708873