# IEEE Transactions on Neural Networks and Learning Systems

## Issue 4 • April 2019

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## Filter Results

Displaying Results 1 - 25 of 30

Publication Year: 2019, Page(s):C1 - 967
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• ### IEEE Transactions on Neural Networks and Learning Systems publication information

Publication Year: 2019, Page(s): C2
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• ### Denoising Adversarial Autoencoders

Publication Year: 2019, Page(s):968 - 984
Cited by:  Papers (1)
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Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabeled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabeled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clean inp... View full abstract»

• ### A Novel Cluster Validity Index Based on Local Cores

Publication Year: 2019, Page(s):985 - 999
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It is critical to evaluate the quality of clusters for most cluster analysis. A number of cluster validity indexes have been proposed, such as the Silhouette and Davies–Bouldin indexes. However, these validity indexes cannot be used to process clusters with arbitrary shapes. Some researchers employ graph-based distance to cluster nonspherical data sets, but the computation of graph-based distances... View full abstract»

• ### Exponential Synchronization for Delayed Dynamical Networks via Intermittent Control: Dealing With Actuator Saturations

Publication Year: 2019, Page(s):1000 - 1012
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Over the past two decades, the synchronization problem for dynamical networks has drawn significant attention due to its clear practical insight in biological systems, social networks, and neuroscience. In the case where a dynamical network cannot achieve the synchronization by itself, the feedback controller should be added to drive the network toward a desired orbit. On the other hand, the time ... View full abstract»

• ### Semantically Modeling of Object and Context for Categorization

Publication Year: 2019, Page(s):1013 - 1024
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Object-centric-based categorization methods have been proven more effective than hard partitions of images (e.g., spatial pyramid matching). However, how to determine the locations of objects is still an open problem. Besides, modeling of context areas is often mixed with the background. Moreover, the semantic information is often ignored by these methods that only use visual representations for c... View full abstract»

• ### Exponential Synchronizationlike Criterion for State-Dependent Impulsive Dynamical Networks

Publication Year: 2019, Page(s):1025 - 1033
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This paper focuses on the problem of the exponential synchronizationlike criteria for state-dependent impulsive dynamical networks (SIDNs). Two types of sufficient conditions, which are applied to ensure every solution intersecting each impulsive surface exactly once, are derived. For each type of collision conditions, combining with comparison principle and inequality techniques, some sufficient ... View full abstract»

• ### Variational Bayesian Learning of Generalized Dirichlet-Based Hidden Markov Models Applied to Unusual Events Detection

Publication Year: 2019, Page(s):1034 - 1047
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Learning a hidden Markov model (HMM) is typically based on the computation of a likelihood which is intractable due to a summation over all possible combinations of states and mixture components. This estimation is often tackled by a maximization strategy, which is known as the Baum–Welch algorithm. However, some drawbacks of this approach have led to the consideration of Bayesian methods that add... View full abstract»

• ### Self-Organizing Neuroevolution for Solving Carpool Service Problem With Dynamic Capacity to Alternate Matches

Publication Year: 2019, Page(s):1048 - 1060
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Traffic congestion often incurs environmental problems. One of the most effective ways to mitigate this is carpooling transportation, which substantially reduces automobile demands. Due to the popularization of smartphones and mobile applications, a carpool service can be conveniently accessed via the intelligent carpool system. In this system, the service optimization required to intelligently an... View full abstract»

• ### Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis

Publication Year: 2019, Page(s):1061 - 1075
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Tensor data (i.e., the data having multiple dimensions) are quickly growing in scale in many practical applications, which poses new challenges for data modeling and analysis approaches, such as high-order relations of large complexity, gross noise, and varying data scale. Existing low-rank data analysis methods, which are effective at analyzing matrix data, may fail in the regime of tensor data d... View full abstract»

• ### Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method

Publication Year: 2019, Page(s):1076 - 1087
Cited by:  Papers (5)
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In this paper, the problem of adaptive neural state-feedback tracking control is considered for a class of stochastic nonstrict-feedback nonlinear switched systems with completely unknown nonlinearities. In the design procedure, the universal approximation capability of radial basis function neural networks is used for identifying the unknown compounded nonlinear functions, and a variable separati... View full abstract»

• ### Active Learning From Imbalanced Data: A Solution of Online Weighted Extreme Learning Machine

Publication Year: 2019, Page(s):1088 - 1103
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It is well known that active learning can simultaneously improve the quality of the classification model and decrease the complexity of training instances. However, several previous studies have indicated that the performance of active learning is easily disrupted by an imbalanced data distribution. Some existing imbalanced active learning approaches also suffer from either low performance or high... View full abstract»

• ### Perception Coordination Network: A Neuro Framework for Multimodal Concept Acquisition and Binding

Publication Year: 2019, Page(s):1104 - 1118
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To simulate the concept acquisition and binding of different senses in the brain, a biologically inspired neural network model named perception coordination network (PCN) is proposed. It is a hierarchical structure, which is functionally divided into the primary sensory area (PSA), the primary sensory association area (SAA), and the higher order association area (HAA). The PSA contains feature neu... View full abstract»

• ### Adaptive Learning Control for Nonlinear Systems With Randomly Varying Iteration Lengths

Publication Year: 2019, Page(s):1119 - 1132
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This paper proposes adaptive iterative learning control (ILC) schemes for continuous-time parametric nonlinear systems with iteration lengths that randomly vary. As opposed to the existing ILC works that feature nonuniform trial lengths, this paper is applicable to nonlinear systems that do not satisfy the globally Lipschitz continuous condition. In addition, this paper introduces a novel composit... View full abstract»

• ### Flexible Affinity Matrix Learning for Unsupervised and Semisupervised Classification

Publication Year: 2019, Page(s):1133 - 1149
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In this paper, we propose a unified model called flexible affinity matrix learning (FAML) for unsupervised and semisupervised classification by exploiting both the relationship among data and the clustering structure simultaneously. To capture the relationship among data, we exploit the self-expressiveness property of data to learn a structured matrix in which the structures are induced by differe... View full abstract»

• ### Fast Inference Predictive Coding: A Novel Model for Constructing Deep Neural Networks

Publication Year: 2019, Page(s):1150 - 1165
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As a biomimetic model of visual information processing, predictive coding (PC) has become increasingly popular for explaining a range of neural responses and many aspects of brain organization. While the development of PC model is encouraging in the neurobiology community, its practical applications in machine learning (e.g., image classification) have not been fully explored yet. In this paper, a... View full abstract»

• ### Nonlinear Dimensionality Reduction With Missing Data Using Parametric Multiple Imputations

Publication Year: 2019, Page(s):1166 - 1179
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Dimensionality reduction (DR) aims at faithfully and meaningfully representing high-dimensional (HD) data into a low-dimensional (LD) space. Recently developed neighbor embedding DR methods lead to outstanding performances, thanks to their ability to foil the curse of dimensionality. Unfortunately, they cannot be directly employed on incomplete data sets, which become ubiquitous in machine learnin... View full abstract»

• ### Domain Adaption via Feature Selection on Explicit Feature Map

Publication Year: 2019, Page(s):1180 - 1190
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In most domain adaption approaches, all features are used for domain adaption. However, often, not every feature is beneficial for domain adaption. In such cases, incorrectly involving all features might cause the performance to degrade. In other words, to make the model trained on the source domain work well on the target domain, it is desirable to find invariant features for domain adaption rath... View full abstract»

• ### Universal Approximation Capability of Broad Learning System and Its Structural Variations

Publication Year: 2019, Page(s):1191 - 1204
Cited by:  Papers (1)
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After a very fast and efficient discriminative broad learning system (BLS) that takes advantage of flatted structure and incremental learning has been developed, here, a mathematical proof of the universal approximation property of BLS is provided. In addition, the framework of several BLS variants with their mathematical modeling is given. The variations include cascade, recurrent, and broad–deep... View full abstract»

• ### Dualityfree Methods for Stochastic Composition Optimization

Publication Year: 2019, Page(s):1205 - 1217
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In this paper, we consider the composition optimization with two expected-value functions in the form of $({1}/{n})\sum _{i = 1}^{n} F_{i}\left({({1}/{m})\sum _{j = 1}^{m} G_{j}(x)}\right)+R(x)$ , which formulates many important problems in statistical learning and machine learning such as solving Bellman equations in reinforc... View full abstract»

• ### Event-Based Line Fitting and Segment Detection Using a Neuromorphic Visual Sensor

Publication Year: 2019, Page(s):1218 - 1230
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This paper introduces an event-based luminance-free algorithm for line and segment detection from the output of asynchronous event-based neuromorphic retinas. These recent biomimetic vision sensors are composed of autonomous pixels, each of them asynchronously generating visual events that encode relative changes in pixels’ illumination at high temporal resolutions. This frame-free approach result... View full abstract»

• ### SEFRON: A New Spiking Neuron Model With Time-Varying Synaptic Efficacy Function for Pattern Classification

Publication Year: 2019, Page(s):1231 - 1240
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This paper presents a new time-varying long-term Synaptic Efficacy Function-based leaky-integrate-and-fire neuRON model, referred to as SEFRON and its supervised learning rule for pattern classification problems. The time-varying synaptic efficacy function is represented by a sum of amplitude modulated Gaussian distribution functions located at different times. For a given pattern, the SEFRON’s le... View full abstract»

• ### Bounded Neural Network Control for Target Tracking of Underactuated Autonomous Surface Vehicles in the Presence of Uncertain Target Dynamics

Publication Year: 2019, Page(s):1241 - 1249
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This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are obtained. First, a kinematic control law is designed based on an extended state observer, which is utilized to estimate the uncertain target dynamics due... View full abstract»

• ### Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data

Publication Year: 2019, Page(s):1250 - 1258
Cited by:  Papers (2)
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In this work, travel destinations and business locations are taken as venues. Discovering a venue by a photograph is very important for visual context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photographs generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a nove... View full abstract»

• ### Markov Boundary-Based Outlier Mining

Publication Year: 2019, Page(s):1259 - 1264
Cited by:  Papers (1)
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It is a grand challenge to identify the outliers existing in subspaces from a high-dimensional data set. A brute-force method is computationally prohibitive since it requires examining an exponential number of subspaces. Current state-of-the-art methods explore various heuristics to significantly prune subspaces, facing the tradeoff between the subspace completeness and search efficiency. In this ... View full abstract»

## Aims & Scope

IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Haibo He
Dept. of Electrical, Computer, and Biomedical Engineering
University of Rhode Island
Kingston, RI 02881, USA
ieeetnnls@gmail.com