# IEEE Transactions on Neural Networks and Learning Systems

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

Publication Year: 2018, Page(s):1774 - 1785
Cited by:  Papers (7)
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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»

• ### LSTM: A Search Space Odyssey

Publication Year: 2017, Page(s):2222 - 2232
Cited by:  Papers (98)
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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»

• ### Hierarchical Deep Reinforcement Learning for Continuous Action Control

Publication Year: 2018, Page(s):5174 - 5184
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Robotic control in a continuous action space has long been a challenging topic. This is especially true when controlling robots to solve compound tasks, as both basic skills and compound skills need to be learned. In this paper, we propose a hierarchical deep reinforcement learning algorithm to learn basic skills and compound skills simultaneously. In the proposed algorithm, compound skills and ba... View full abstract»

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

Publication Year: 2018, Page(s):2063 - 2079
Cited by:  Papers (8)
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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 (261)
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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 (10)
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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»

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

Publication Year: 2018, Page(s):2042 - 2062
Cited by:  Papers (1)
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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»

Publication Year: 2018, Page(s):5475 - 5485
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In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons. We demonstrate our algorithm on networks of convolutional layers, though its ap... 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»

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

Publication Year: 2017, Page(s):653 - 664
Cited by:  Papers (28)
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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 Neural Network for Structural Prediction and Lane Detection in Traffic Scene

Publication Year: 2017, Page(s):690 - 703
Cited by:  Papers (49)
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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»

• ### Approximate Low-Rank Projection Learning for Feature Extraction

Publication Year: 2018, Page(s):5228 - 5241
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Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised feature extraction method, latent low-rank representation (LatLRR) has shown its power in extracting salient features. However, LatLRR has the following three disadvantages: 1)... View full abstract»

• ### A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks

Publication Year: 2018, Page(s):5394 - 5407
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There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a n... View full abstract»

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

Publication Year: 2016, Page(s):1643 - 1656
Cited by:  Papers (41)
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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»

• ### Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks

Publication Year: 2018, Page(s):5784 - 5789
Cited by:  Papers (4)
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Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and speech recognition, that potentially enable numerous applications. Consequently, there is a significant need to deploy trained CNNs to resource-constrained embedded systems. Inference using pretrained modern deep CNNs, however, requires significa... View full abstract»

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

Publication Year: 2018, Page(s):3573 - 3587
Cited by:  Papers (10)
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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»

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

Publication Year: 2014, Page(s):845 - 869
Cited by:  Papers (210)
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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»

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

Publication Year: 2015, Page(s):1019 - 1034
Cited by:  Papers (84)
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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»

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

Publication Year: 2014, Page(s):2303 - 2308
Cited by:  Papers (63)
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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»

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

Publication Year: 2017, Page(s):2660 - 2673
Cited by:  Papers (17)
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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»

• ### Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks

Publication Year: 2018, Page(s):5619 - 5629
Cited by:  Papers (3)
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Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain-computer interfaces (BCIs), in order to boost classification performance, is still limited. In this paper, we propose a classification framework for MI dat... View full abstract»

• ### Optimizing Kernel Machines Using Deep Learning

Publication Year: 2018, Page(s):5528 - 5540
Cited by:  Papers (1)
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Building highly nonlinear and nonparametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that induce a reproducing kernel Hilbert space (RKHS) for inferring non-linear models through the construction of similarity functions from data. These methods are particularly preferred in cases where the training data sizes are l... View full abstract»

• ### F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

Publication Year: 2018, Page(s):5185 - 5199
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The generalization error bound of the support vector machine (SVM) depends on the ratio of the radius and margin. However, conventional SVM only considers the maximization of the margin but ignores the minimization of the radius, which restricts its performance when applied to joint learning of feature transformation and the SVM classifier. Although several approaches have been proposed to integra... View full abstract»

• ### Convolution in Convolution for Network in Network

Publication Year: 2018, Page(s):1587 - 1597
Cited by:  Papers (3)
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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»

• ### Blind Image Quality Assessment via Deep Learning

Publication Year: 2015, Page(s):1275 - 1286
Cited by:  Papers (88)
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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»

• ### 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»

• ### Efficient Reinforcement Learning via Probabilistic Trajectory Optimization

Publication Year: 2018, Page(s):5459 - 5474
Cited by:  Papers (1)
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We present a trajectory optimization approach to reinforcement learning in continuous state and action spaces, called probabilistic differential dynamic programming (PDDP). Our method represents systems dynamics using Gaussian processes (GPs), and performs local dynamic programming iteratively around a nominal trajectory in Gaussian belief spaces. Different from model-based policy search methods, ... View full abstract»

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

Publication Year: 2016, Page(s):1773 - 1786
Cited by:  Papers (41)
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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»

• ### 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 (297)
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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»

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

Publication Year: 2016, Page(s):125 - 138
Cited by:  Papers (80)
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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 Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

Publication Year: 2018, Page(s):545 - 559
Cited by:  Papers (10)
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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»

• ### Fuzzy Neural Network Control of a Flexible Robotic Manipulator Using Assumed Mode Method

Publication Year: 2018, Page(s):5214 - 5227
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In this paper, in order to analyze the single-link flexible structure, the assumed mode method is employed to develop the dynamic model. Based on the discrete dynamic model, fuzzy neural network (NN) control is investigated to track the desired trajectory accurately and to suppress the flexible vibration maximally. To ensure the stability rigorously as the goal, the system is proved to be uniform ... 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)
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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»

• ### Integrating Space, Time, and Orientation in Spiking Neural Networks: A Case Study on Multimodal Brain Data Modeling

Publication Year: 2018, Page(s):5249 - 5263
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Recent progress in a noninvasive brain data sampling technology has facilitated simultaneous sampling of multiple modalities of brain data, such as functional magnetic resonance imaging, electroencephalography, diffusion tensor imaging, and so on. In spite of the potential benefits from integrating predictive modeling of multiple modality brain data, this area of research remains mostly unexplored... View full abstract»

• ### Theoretical Study of Oscillator Neurons in Recurrent Neural Networks

Publication Year: 2018, Page(s):5242 - 5248
Cited by:  Papers (1)
| | PDF (1080 KB) | HTML

Neurons in a network can be both active or inactive. Given a subset of neurons in a network, is it possible for the subset of neurons to evolve to form an active oscillator by applying some external periodic stimulus? Furthermore, can these oscillator neurons be observable, that is, is it a stable oscillator? This paper explores such possibility, finding that an important property: any subset of n... View full abstract»

• ### Deep Hyperspectral Image Sharpening

Publication Year: 2018, Page(s):5345 - 5355
Cited by:  Papers (4)
| | PDF (3823 KB) | HTML

Hyperspectral image (HSI) sharpening, which aims at fusing an observable low spatial resolution (LR) HSI (LR-HSI) with a high spatial resolution (HR) multispectral image (HR-MSI) of the same scene to acquire an HR-HSI, has recently attracted much attention. Most of the recent HSI sharpening approaches are based on image priors modeling, which are usually sensitive to the parameters selection and t... View full abstract»

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

Publication Year: 2014, Page(s):1864 - 1878
Cited by:  Papers (105)
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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»

• ### Adaptive Learning-Based$k$-Nearest Neighbor Classifiers With Resilience to Class Imbalance

Publication Year: 2018, Page(s):5713 - 5725
| | PDF (1708 KB) | HTML Media

The classification accuracy of a k-nearest neighbor (kNN) classifier is largely dependent on the choice of the number of nearest neighbors denoted by k. However, given a data set, it is a tedious task to optimize the performance of kNN by tuning k. Moreover, the performance of kNN degrades in the presence of class imbalance, a situation characterized by disparate representation from different clas... View full abstract»

• ### Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone

Publication Year: 2018, Page(s):5200 - 5213
Cited by:  Papers (1)
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This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to de... 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»

• ### Structure Learning for Deep Neural Networks Based on Multiobjective Optimization

Publication Year: 2018, Page(s):2450 - 2463
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This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible... View full abstract»

• ### Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks

Publication Year: 2018, Page(s):4730 - 4743
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We are witnessing an explosive development and widespread application of deep neural networks (DNNs) in various fields. However, DNN models, especially a convolutional neural network (CNN), usually involve massive parameters and are computationally expensive, making them extremely dependent on high-performance hardware. This prohibits their further extensions, e.g., applications on mobile devices.... 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 (226)
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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»

• ### Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

Publication Year: 2018, Page(s):1174 - 1186
Cited by:  Papers (26)
| | PDF (1793 KB) | HTML

This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there... View full abstract»

• ### Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data

Publication Year: 2018, Page(s):5304 - 5318
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The tree structure is one of the most powerful structures for data organization. An efficient learning framework for transforming tree-structured data into vectorial representations is presented. First, in attempting to uncover the global discriminative information of child nodes hidden at the same level of all of the trees, a clustering technique can be adopted for allocating children into differ... View full abstract»

• ### Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties

Publication Year: 2018, Page(s):5554 - 5564
Cited by:  Papers (5)
| | PDF (2524 KB) | HTML

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adop... View full abstract»

• ### Semisupervised Negative Correlation Learning

Publication Year: 2018, Page(s):5366 - 5379
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Negative correlation learning (NCL) is an ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual ensemble member. Each ensemble member minimizes its mean square error and its error correlation with the rest of the ensemble. This paper analyzes NCL and reveals that adopting a negative correlation term for unlabeled data is beneficial to improv... View full abstract»

• ### Multiview Clustering via Unified and View-Specific Embeddings Learning

Publication Year: 2018, Page(s):5541 - 5553
| | PDF (2138 KB) | HTML

Multiview clustering, which aims at using multiple distinct feature sets to boost clustering performance, has a wide range of applications. A subspace-based approach, a type of widely used methods, learns unified embedding from multiple sources of information and gives a relatively good performance. However, these methods usually ignore data similarity rankings; for example, example A may be more ... View full abstract»

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

Publication Year: 2015, Page(s):2222 - 2233
Cited by:  Papers (65)
| | 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»

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

Publication Year: 2018, Page(s):2239 - 2252
| | PDF (3518 KB) | HTML

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»

## 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