# IEEE Transactions on Neural Networks and Learning Systems

## Issue 9 • Sept. 2018

The purchase and pricing options for this item are unavailable. Select items are only available as part of a subscription package. You may try again later or contact us for more information.

## Filter Results

Displaying Results 1 - 25 of 58

Publication Year: 2018, Page(s):C1 - 3925
| PDF (126 KB)
• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2018, Page(s): C2
| PDF (119 KB)
• ### Continuous Dropout

Publication Year: 2018, Page(s):3926 - 3937
| | PDF (2690 KB)

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»

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

Publication Year: 2018, Page(s):3938 - 3952
| | PDF (2940 KB)

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»

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

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

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

Publication Year: 2018, Page(s):3969 - 3979
| | PDF (2720 KB)

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»

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

Publication Year: 2018, Page(s):3980 - 3993
| | PDF (3725 KB) |  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»

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

Publication Year: 2018, Page(s):3994 - 4006
| | PDF (2165 KB)

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»

• ### 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)

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»

• ### Detection of Sources in Non-Negative Blind Source Separation by Minimum Description Length Criterion

Publication Year: 2018, Page(s):4022 - 4037
Cited by:  Papers (1)
| | PDF (2738 KB)

While non-negative blind source separation (nBSS) has found many successful applications in science and engineering, model order selection, determining the number of sources, remains a critical yet unresolved problem. Various model order selection methods have been proposed and applied to real-world data sets but with limited success, with both order over- and under-estimation reported. By studyin... View full abstract»

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

Publication Year: 2018, Page(s):4038 - 4050
| | PDF (2085 KB)

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»

• ### Heterogeneous Multitask Metric Learning Across Multiple Domains

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

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

Publication Year: 2018, Page(s):4065 - 4076
Cited by:  Papers (1)
| | PDF (2134 KB)

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»

• ### A Novel Error-Compensation Control for a Class of High-Order Nonlinear Systems With Input Delay

Publication Year: 2018, Page(s):4077 - 4087
| | PDF (1573 KB)

A novel tracking error-compensation-based adaptive neural control scheme is proposed for a class of high-order nonlinear systems with completely unknown nonlinearities and input delay. In the tracking errors of existing papers, there exist the following difficulties: first, output curve always lags behind the desired trajectory, second, some big peak errors cause a decrease in tracking precision, ... View full abstract»

• ### Dimensionality Reduction in Multiple Ordinal Regression

Publication Year: 2018, Page(s):4088 - 4101
| | PDF (4282 KB)

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»

• ### 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
| | PDF (1726 KB)

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»

• ### Transductive Zero-Shot Learning With Adaptive Structural Embedding

Publication Year: 2018, Page(s):4116 - 4127
| | PDF (2262 KB)

Zero-shot learning (ZSL) endows the computer vision system with the inferential capability to recognize new categories that have never seen before. Two fundamental challenges in it are visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps, respectively. This paper presents two corresponding methods named Adaptive STructural Embedding (ASTE) an... View full abstract»

• ### Bayesian Nonparametric Regression Modeling of Panel Data for Sequential Classification

Publication Year: 2018, Page(s):4128 - 4139
| | PDF (3339 KB)

This paper proposes a Bayesian nonparametric regression model of panel data for sequential pattern classification. The proposed method provides a flexible and parsimonious model that allows both time-independent spatial variables and time-dependent exogenous variables to be predictors. Not only this method improves the accuracy of parameter estimation for limited data, but also it facilitates mode... View full abstract»

• ### Symmetric Predictive Estimator for Biologically Plausible Neural Learning

Publication Year: 2018, Page(s):4140 - 4151
| | PDF (3724 KB)

In a real brain, the act of perception is a bidirectional process, depending on both feedforward sensory pathways and feedback pathways that carry expectations. We are interested in how such a neural network might emerge from a biologically plausible learning rule. Other neural network learning methods either only apply to feedforward networks, or employ assumptions (such as weight copying) that r... 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)
| | PDF (6821 KB)

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»

• ### Learning With Coefficient-Based Regularized Regression on Markov Resampling

Publication Year: 2018, Page(s):4166 - 4176
| | PDF (1506 KB)

Big data research has become a globally hot topic in recent years. One of the core problems in big data learning is how to extract effective information from the huge data. In this paper, we propose a Markov resampling algorithm to draw useful samples for handling coefficient-based regularized regression (CBRR) problem. The proposed Markov resampling algorithm is a selective sampling method, which... View full abstract»

• ### Sequential Labeling With Structural SVM Under Nondecomposable Losses

Publication Year: 2018, Page(s):4177 - 4188
| | PDF (2436 KB)

Sequential labeling addresses the classification of sequential data, which are widespread in fields as diverse as computer vision, finance, and genomics. The model traditionally used for sequential labeling is the hidden Markov model (HMM), where the sequence of class labels to be predicted is encoded as a Markov chain. In recent years, HMMs have benefited from minimum-loss training approaches, su... View full abstract»

• ### The Stability of Stochastic Coupled Systems With Time-Varying Coupling and General Topology Structure

Publication Year: 2018, Page(s):4189 - 4200
| | PDF (1299 KB)

We introduce a class of novel stochastic coupled systems in which the coupling structure is time-varying and the topology structure is not strongly connected, and first establish the system on a digraph with a time-varying weight matrix. Motivated by Du and Li (2014), we give a hierarchical method to deal with digraphs without strong connectivity and establish the corresponding hierarchical algori... View full abstract»

• ### Stability Analysis of Quaternion-Valued Neural Networks: Decomposition and Direct Approaches

Publication Year: 2018, Page(s):4201 - 4211
| | PDF (1350 KB)

In this paper, we investigate the global stability of quaternion-valued neural networks (QVNNs) with time-varying delays. On one hand, in order to avoid the noncommutativity of quaternion multiplication, the QVNN is decomposed into four real-valued systems based on Hamilton rules:$ij=-ji=k,~jk=-kj=i$, View full abstract»

• ### On Wang$k$WTA With Input Noise, Output Node Stochastic, and Recurrent State Noise

Publication Year: 2018, Page(s):4212 - 4222
| | PDF (2004 KB)

In this paper, the effect of input noise, output node stochastic, and recurrent state noise on the Wang$k$WTA is analyzed. Here, we assume that noise exists at the recurrent state$y(t)$and it can either be additive or multiplicative. Besides, its dynamical c... 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