# IEEE Transactions on Neural Networks and Learning Systems

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• ### 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 (3)
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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 (12)
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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»

• ### Extreme Learning Machine for Multilayer Perceptron

Publication Year: 2016, Page(s):809 - 821
Cited by:  Papers (105)
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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»

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

Publication Year: 2017, Page(s):653 - 664
Cited by:  Papers (2)
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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»

• ### Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Publication Year: 2018, Page(s):681 - 694
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In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the perform... View full abstract»

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

Publication Year: 2018, Page(s):757 - 762
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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»

• ### 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 (3)
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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»

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

Publication Year: 2017, Page(s):690 - 703
Cited by:  Papers (7)
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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»

• ### Robust C-Loss Kernel Classifiers

Publication Year: 2018, Page(s):510 - 522
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The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel clas... 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 (30)
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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»

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

Publication Year: 2017, Page(s):2660 - 2673
Cited by:  Papers (3)
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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»

• ### A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization

Publication Year: 2018, Page(s):534 - 544
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In this paper, based on CR calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimiza... View full abstract»

• ### Synchronization of General Chaotic Neural Networks With Nonuniform Sampling and Packet Missing: A Switched System Approach

Publication Year: 2018, Page(s):523 - 533
Cited by:  Papers (1)
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This paper is concerned with the exponential synchronization issue of general chaotic neural networks subject to nonuniform sampling and control packet missing in the frame of the zero-input strategy. Based on this strategy, we make use of the switched system model to describe the synchronization error system. First, when the missing of control packet does not occur, an exponential stability crite... View full abstract»

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

Publication Year: 2018, Page(s):1 - 17
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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»

• ### Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Publication Year: 2017, Page(s):2371 - 2381
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Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network a... View full abstract»

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

Publication Year: 2016, Page(s):2351 - 2363
Cited by:  Papers (2)
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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»

• ### Probabilistic Low-Rank Multitask Learning

Publication Year: 2018, Page(s):670 - 680
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In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multit... View full abstract»

• ### Multicolumn RBF Network

Publication Year: 2018, Page(s):766 - 778
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This paper proposes the multicolumn RBF network (MCRN) as a method to improve the accuracy and speed of a traditional radial basis function network (RBFN). The RBFN, as a fully connected artificial neural network (ANN), suffers from costly kernel inner-product calculations due to the use of many instances as the centers of hidden units. This issue is not critical for small datasets, as adding more... 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»

• ### Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis

Publication Year: 2018, Page(s):560 - 572
Cited by:  Papers (2)
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Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal comp... View full abstract»

• ### Stabilization of Neural-Network-Based Control Systems via Event-Triggered Control With Nonperiodic Sampled Data

Publication Year: 2018, Page(s):573 - 585
Cited by:  Papers (1)
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This paper focuses on a problem of event-triggered stabilization for a class of nonuniformly sampled neural-network-based control systems (NNBCSs). First, a new event-triggered data transmission mechanism is designed based on the nonperiodic sampled data. Different from the previous works, the proposed triggering scheme enables the NNBCSs design to enjoy the advantages of both nonuniform and event... View full abstract»

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

Publication Year: 2018, Page(s):1 - 11
| | PDF (2359 KB)

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»

• ### Self-Taught Low-Rank Coding for Visual Learning

Publication Year: 2018, Page(s):645 - 656
Cited by:  Papers (1)
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The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semisupervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively. Self-taught learning, which is a special type of transfer learning, has fewer restrictions on the choi... View full abstract»

• ### Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

Publication Year: 2017, Page(s):523 - 533
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Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from multiple track circuits in a geographic area, faults ar... View full abstract»

• ### Global Asymptotic Stability and Stabilization of Neural Networks With General Noise

Publication Year: 2018, Page(s):597 - 607
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Neural networks (NNs) in the stochastic environment were widely modeled as stochastic differential equations, which were driven by white noise, such as Brown or Wiener process in the existing papers. However, they are not necessarily the best models to describe dynamic characters of NNs disturbed by nonwhite noise in some specific situations. In this paper, general noise disturbance, which may be ... View full abstract»

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

Publication Year: 2018, Page(s):1 - 14
| | PDF (3592 KB)

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»

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

Publication Year: 2018, Page(s):1 - 11
| | PDF (2872 KB)

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»

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

Publication Year: 2017, Page(s):1 - 21
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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 H₂ and 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 measured... View full abstract» • ### Cooperative Adaptive Output Regulation for Second-Order Nonlinear Multiagent Systems With Jointly Connected Switching Networks Publication Year: 2018, Page(s):695 - 705 | | PDF (1382 KB) | HTML This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed join... View full abstract» • ### Optimizing Kernel Machines Using Deep Learning Publication Year: 2018, Page(s):1 - 13 | | PDF (3623 KB) 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» • ### Terminal Sliding Mode-Based Consensus Tracking Control for Networked Uncertain Mechanical Systems on Digraphs Publication Year: 2018, Page(s):749 - 756 | | PDF (838 KB) | HTML This brief investigates the finite-time consensus tracking control problem for networked uncertain mechanical systems on digraphs. A new terminal sliding-mode-based cooperative control scheme is developed to guarantee that the tracking errors converge to an arbitrarily small bound around zero in finite time. All the networked systems can have different dynamics and all the dynamics are unknown. A ... View full abstract» • ### Dynamic Energy Management System for a Smart Microgrid Publication Year: 2016, Page(s):1643 - 1656 Cited by: Papers (14) | | PDF (3285 KB) | HTML 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» • ### A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks Publication Year: 2018, Page(s):1 - 14 | | PDF (2790 KB) 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» • ### Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics Publication Year: 2017, Page(s):2306 - 2318 | | PDF (3316 KB) | HTML In numerous industrial applications where safety, efficiency, and reliability are among primary concerns, condition-based maintenance (CBM) is often the most effective and reliable maintenance policy. Prognostics, as one of the key enablers of CBM, involves the core task of estimating the remaining useful life (RUL) of the system. Neural networks-based approaches have produced promising results on... View full abstract» • ### A Survey of Memristive Threshold Logic Circuits Publication Year: 2017, Page(s):1734 - 1746 Cited by: Papers (1) | | PDF (3721 KB) | HTML In this paper, we review different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike generalization ability and the area minimization of these threshold logic circuits aim toward crossing Moore's law boundaries at device, circuits, and systems levels. Fast switching memory, signal processing,... View full abstract» • ### Feature Combination via Clustering Publication Year: 2018, Page(s):896 - 907 | | PDF (1581 KB) | HTML In image classification, feature combination is often used to combine the merits of multiple complementary features and improve the classification accuracy compared with one single feature. Existing feature combination algorithms, e.g., multiple kernel learning, usually determine the weights of features based on the optimization with respect to some classifier-dependent objective function. These a... View full abstract» • ### Insights Into the Robustness of Minimum Error Entropy Estimation Publication Year: 2018, Page(s):731 - 737 | | PDF (1112 KB) | HTML The minimum error entropy (MEE) is an important and highly effective optimization criterion in information theoretic learning (ITL). For regression problems, MEE aims at minimizing the entropy of the prediction error such that the estimated model preserves the information of the data generating system as much as possible. In many real world applications, the MEE estimator can outperform significan... View full abstract» • ### Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals Publication Year: 2014, Page(s):303 - 315 Cited by: Papers (88) | | PDF (1440 KB) | HTML Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load de... View full abstract» • ### Robust DLPP With Nongreedy$ell _1\$ -Norm Minimization and Maximization

Publication Year: 2018, Page(s):738 - 743
| | PDF (847 KB) | HTML

Recently, discriminant locality preserving projection based on L1-norm (DLPP-L1) was developed for robust subspace learning and image classification. It obtains projection vectors by greedy strategy, i.e., all projection vectors are optimized individually through maximizing the objective function. Thus, the obtained solution does not necessarily best optimize the corresponding trace ratio optimiza... View full abstract»

• ### On Deep Learning for Trust-Aware Recommendations in Social Networks

Publication Year: 2017, Page(s):1164 - 1177
Cited by:  Papers (2)
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With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommend... View full abstract»

• ### Feature Selection Based on Structured Sparsity: A Comprehensive Study

Publication Year: 2017, Page(s):1490 - 1507
Cited by:  Papers (13)
| | PDF (3835 KB) | HTML

Feature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have ... 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 (230)
| | PDF (1316 KB) | HTML

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 Td is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the... View full abstract»

• ### Leader-Follower Output Synchronization of Linear Heterogeneous Systems With Active Leader Using Reinforcement Learning

Publication Year: 2018, Page(s):1 - 15
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This paper develops optimal control protocols for the distributed output synchronization problem of leader-follower multiagent systems with an active leader. Agents are assumed to be heterogeneous with different dynamics and dimensions. The desired trajectory is assumed to be preplanned and is generated by the leader. Other follower agents autonomously synchronize to the leader by interacting with... View full abstract»

Publication Year: 2018, Page(s):1 - 11
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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»

• ### A Fast Algorithm of Convex Hull Vertices Selection for Online Classification

Publication Year: 2018, Page(s):792 - 806
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Reducing samples through convex hull vertices selection (CHVS) within each class is an important and effective method for online classification problems, since the classifier can be trained rapidly with the selected samples. However, the process of CHVS is NP-hard. In this paper, we propose a fast algorithm to select the convex hull vertices, based on the convex hull decomposition and the property... View full abstract»

• ### Supervised Discrete Hashing With Relaxation

Publication Year: 2018, Page(s):608 - 617
Cited by:  Papers (2)
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Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called “supervised discrete hashing with relaxation” (SDHR) based on “supervised discrete hashing” (SDH). SDH uses ordinary le... View full abstract»

• ### Dissipativity Analysis for Stochastic Memristive Neural Networks With Time-Varying Delays: A Discrete-Time Case

Publication Year: 2018, Page(s):618 - 630
Cited by:  Papers (2)
| | PDF (1190 KB) | HTML

In this paper, the dissipativity problem of discretetime memristive neural networks (DMNNs) with time-varying delays and stochastic perturbation is investigated. A class of logical switched functions are put forward to reflect the memristor-based switched property of connection weights, and the DMNNs are then recast into a tractable model. Based on the tractable model, the robust analysis method a... View full abstract»

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

Publication Year: 2017, Page(s):1 - 19
| | PDF (3027 KB) |  Media

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»

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

Publication Year: 2014, Page(s):845 - 869
Cited by:  Papers (118)
| | PDF (1806 KB) | HTML

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»

• ### Multiview Boosting With Information Propagation for Classification

Publication Year: 2018, Page(s):657 - 669
| | PDF (1352 KB) | HTML

Multiview learning has shown promising potential in many applications. However, most techniques are focused on either view consistency, or view diversity. In this paper, we introduce a novel multiview boosting algorithm, called Boost.SH, that computes weak classifiers independently of each view but uses a shared weight distribution to propagate information among the multiple views to ensure consis... 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