# IEEE Transactions on Neural Networks and Learning Systems

Includes the top 50 most frequently accessed documents for this publication according to the usage statistics for the month of

• ### Efficient kNN Classification With Different Numbers of Nearest Neighbors

Publication Year: 2018, Page(s):1774 - 1785
Cited by:  Papers (5)
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k nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed k value (even though set by experts) to all test samples. Previous solutions assign different k values to different test samples by the cross validat... View full abstract»

• ### Applications of Deep Learning and Reinforcement Learning to Biological Data

Publication Year: 2018, Page(s):2063 - 2079
Cited by:  Papers (5)
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Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent re... View full abstract»

• ### Extreme Learning Machine for Multilayer Perceptron

Publication Year: 2016, Page(s):809 - 821
Cited by:  Papers (224)
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Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number... View full abstract»

• ### Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

Publication Year: 2018, Page(s):10 - 24
Cited by:  Papers (5)
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Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is establis... View full abstract»

• ### LSTM: A Search Space Odyssey

Publication Year: 2017, Page(s):2222 - 2232
Cited by:  Papers (62)
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Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In t... View full abstract»

• ### Optimal and Autonomous Control Using Reinforcement Learning: A Survey

Publication Year: 2018, Page(s):2042 - 2062
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This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal H2and H∞control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using mea... View full abstract»

• ### Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

Publication Year: 2017, Page(s):653 - 664
Cited by:  Papers (23)
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Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automa... View full abstract»

• ### Deep Manifold Learning Combined With Convolutional Neural Networks for Action Recognition

Publication Year: 2018, Page(s):3938 - 3952
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Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DM... View full abstract»

• ### Learning Deep and Wide: A Spectral Method for Learning Deep Networks

Publication Year: 2014, Page(s):2303 - 2308
Cited by:  Papers (61)
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Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many computer vision-related tasks. We propose the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative manifolds into a compact representation. The low-dim... View full abstract»

• ### Continuous Dropout

Publication Year: 2018, Page(s):3926 - 3937
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Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors. Current explanations of dropout include bagging, naive Bayes, regularization, and sex in evolution. According to the activation patterns of neurons in the human brain, when faced with different situations, the firin... View full abstract»

• ### Convolution in Convolution for Network in Network

Publication Year: 2018, Page(s):1587 - 1597
Cited by:  Papers (2)
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Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and 1 × 1 convolutions in spatia... View full abstract»

• ### Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data

Publication Year: 2018, Page(s):3969 - 3979
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Unsupervised feature extractors are known to perform an efficient and discriminative representation of data. Insight into the mappings they perform and human ability to understand them, however, remain very limited. This is especially prominent when multilayer deep learning architectures are used. This paper demonstrates how to remove these bottlenecks within the architecture of non-negativity con... View full abstract»

• ### Evaluating the Visualization of What a Deep Neural Network Has Learned

Publication Year: 2017, Page(s):2660 - 2673
Cited by:  Papers (14)
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Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches... View full abstract»

• ### Support Vector Data Descriptions and$k$-Means Clustering: One Class?

Publication Year: 2018, Page(s):3994 - 4006
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We presentClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and$k$-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility throu... View full abstract»

• ### Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming

Publication Year: 2016, Page(s):2351 - 2363
Cited by:  Papers (3)
| | PDF (2319 KB) | HTML

A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections ba... View full abstract»

• ### Classification of Imbalanced Data by Oversampling in Kernel Space of Support Vector Machines

Publication Year: 2018, Page(s):4065 - 4076
Cited by:  Papers (1)
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Historical data sets for fault stage diagnosis in industrial machines are often imbalanced and consist of multiple categories or classes. Learning discriminative models from such data sets is challenging due to the lack of representative data and the bias of traditional classifiers toward the majority class. Sampling methods like synthetic minority oversampling technique (SMOTE) have been traditio... View full abstract»

• ### Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis

Publication Year: 2018, Page(s):3980 - 3993
Cited by:  Papers (2)
| | PDF (3725 KB) | HTML Media

Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators’ load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning al... View full abstract»

• ### Classification in the Presence of Label Noise: A Survey

Publication Year: 2014, Page(s):845 - 869
Cited by:  Papers (179)
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Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the ... View full abstract»

• ### Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

Publication Year: 2018, Page(s):3573 - 3587
Cited by:  Papers (10)
| | PDF (3780 KB) | HTML Media

Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes are abundant, making them an overrepresented majority, and data of other classes are scarce, making them an underrepresented minority. This imbalance makes it challenging for a classifier to appropriately learn the discriminating boundaries of the majority and minority clas... View full abstract»

• ### Dynamic Energy Management System for a Smart Microgrid

Publication Year: 2016, Page(s):1643 - 1656
Cited by:  Papers (34)
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This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are s... View full abstract»

• ### Transfer Learning for Visual Categorization: A Survey

Publication Year: 2015, Page(s):1019 - 1034
Cited by:  Papers (71)
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Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future dat... View full abstract»

• ### Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene

Publication Year: 2017, Page(s):690 - 703
Cited by:  Papers (31)
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Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for succe... View full abstract»

• ### A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network

Publication Year: 2018, Page(s):4102 - 4115
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This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are pr... View full abstract»

• ### Deep Learning in Microscopy Image Analysis: A Survey

Publication Year: 2018, Page(s):4550 - 4568
Cited by:  Papers (3)
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Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerging as a leading machine learning tool in computer vision and has attracted considerable attention in biomedical image analysis. In this paper, we provide a sna... View full abstract»

• ### A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control

Publication Year: 2016, Page(s):416 - 425
Cited by:  Papers (283)
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This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T<sub>d</sub> is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoint... View full abstract»

• ### A Distance-Based Weighted Undersampling Scheme for Support Vector Machines and its Application to Imbalanced Classification

Publication Year: 2018, Page(s):4152 - 4165
Cited by:  Papers (1)
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A support vector machine (SVM) plays a prominent role in classic machine learning, especially classification and regression. Through its structural risk minimization, it has enjoyed a good reputation in effectively reducing overfitting, avoiding dimensional disaster, and not falling into local minima. Nevertheless, existing SVMs do not perform well when facing class imbalance and large-scale sampl... View full abstract»

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

Publication Year: 2018, Page(s):1 - 14
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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»

• ### Heterogeneous Multitask Metric Learning Across Multiple Domains

Publication Year: 2018, Page(s):4051 - 4064
Cited by:  Papers (1)
| | PDF (2462 KB) | HTML Media

Distance metric learning plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in a target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multitask metric learning (MTML), which can be regarded as a special case of TML, performs transfer across all ... View full abstract»

• ### Machine Learning Methods for Attack Detection in the Smart Grid

Publication Year: 2016, Page(s):1773 - 1786
Cited by:  Papers (28)
| | PDF (2941 KB) | HTML

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the syste... View full abstract»

• ### Blind Image Quality Assessment via Deep Learning

Publication Year: 2015, Page(s):1275 - 1286
Cited by:  Papers (80)
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This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, ... View full abstract»

• ### Action-Driven Visual Object Tracking With Deep Reinforcement Learning

Publication Year: 2018, Page(s):2239 - 2252
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In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and... View full abstract»

• ### A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

Publication Year: 2018, Page(s):545 - 559
Cited by:  Papers (9)
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We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. Th... View full abstract»

• ### Large-Scale Metric Learning: A Voyage From Shallow to Deep

Publication Year: 2018, Page(s):4339 - 4346
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Despite its attractive properties, the performance of the recently introduced Keep It Simple and Straightforward MEtric learning (KISSME) method is greatly dependent on principal component analysis as a preprocessing step. This dependence can lead to difficulties, e.g., when the dimensionality is not meticulously set. To address this issue, we devise a unified formulation for joint dimensionality ... View full abstract»

• ### Dimensionality Reduction in Multiple Ordinal Regression

Publication Year: 2018, Page(s):4088 - 4101
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Supervised dimensionality reduction (DR) plays an important role in learning systems with high-dimensional data. It projects the data into a low-dimensional subspace and keeps the projected data distinguishable in different classes. In addition to preserving the discriminant information for binary or multiple classes, some real-world applications also require keeping the preference degrees of assi... View full abstract»

• ### Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

Publication Year: 2018, Page(s):3784 - 3797
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Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transa... View full abstract»

• ### Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

Publication Year: 2016, Page(s):125 - 138
Cited by:  Papers (68)
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This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a differen... View full abstract»

• ### A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks

Publication Year: 2014, Page(s):1229 - 1262
Cited by:  Papers (208)
| | PDF (1190 KB) | HTML

Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is ine... View full abstract»

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

Publication Year: 2018, Page(s):1 - 13
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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»

• ### Multiview Multitask Gaze Estimation With Deep Convolutional Neural Networks

Publication Year: 2018, Page(s):1 - 14
| | PDF (3431 KB)

Gaze estimation, which aims to predict gaze points with given eye images, is an important task in computer vision because of its applications in human visual attention understanding. Many existing methods are based on a single camera, and most of them only focus on either the gaze point estimation or gaze direction estimation. In this paper, we propose a novel multitask method for the gaze point e... View full abstract»

• ### AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data

Publication Year: 2018, Page(s):3953 - 3968
| | PDF (2973 KB) | HTML Media

Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its ... View full abstract»

• ### Efficient Online Learning Algorithms Based on LSTM Neural Networks

Publication Year: 2018, Page(s):3772 - 3783
Cited by:  Papers (1)
| | PDF (1375 KB) | HTML

We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filt... View full abstract»

• ### Nonparametric Coupled Bayesian Dictionary and Classifier Learning for Hyperspectral Classification

Publication Year: 2018, Page(s):4038 - 4050
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We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictiona... View full abstract»

• ### DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks

Publication Year: 2018, Page(s):1441 - 1453
Cited by:  Papers (1)
| | PDF (1995 KB) | HTML

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limi... View full abstract»

• ### Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks

Publication Year: 2018, Page(s):246 - 258
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Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whe... View full abstract»

• ### Online Supervised Learning for Hardware-Based Multilayer Spiking Neural Networks Through the Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity

Publication Year: 2018, Page(s):4287 - 4302
Cited by:  Papers (2)
| | PDF (2636 KB) | HTML

In this paper, we propose an online learning algorithm for supervised learning in multilayer spiking neural networks (SNNs). It is found that the spike timings of neurons in an SNN can be exploited to estimate the gradients that are associated with each synapse. With the proposed method of estimating gradients, learning similar to the stochastic gradient descent process employed in a conventional ... View full abstract»

• ### Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Publication Year: 2018, Page(s):4007 - 4021
| | PDF (3064 KB) | HTML

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for... View full abstract»

• ### Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Publication Year: 2018, Page(s):1301 - 1313
Cited by:  Papers (24)
| | PDF (4175 KB) | HTML

In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual qu... View full abstract»

• ### Scene Recognition by Manifold Regularized Deep Learning Architecture

Publication Year: 2015, Page(s):2222 - 2233
Cited by:  Papers (57)
| | PDF (4686 KB) | HTML

Scene recognition is an important problem in the field of computer vision, because it helps to narrow the gap between the computer and the human beings on scene understanding. Semantic modeling is a popular technique used to fill the semantic gap in scene recognition. However, most of the semantic modeling approaches learn shallow, one-layer representations for scene recognition, while ignoring th... View full abstract»

• ### Memristor Crossbar-Based Neuromorphic Computing System: A Case Study

Publication Year: 2014, Page(s):1864 - 1878
Cited by:  Papers (93)
| | PDF (2566 KB) | HTML

By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated... View full abstract»

• ### Kernel-Based Multilayer Extreme Learning Machines for Representation Learning

Publication Year: 2018, Page(s):757 - 762
| | PDF (1152 KB) | HTML

Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training tim... 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