IEEE Transactions on Neural Networks and Learning Systems

Issue 10 • Oct. 2016

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Displaying Results 1 - 16 of 16
  • Table of contents

    Publication Year: 2016, Page(s): C1
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  • IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

    Publication Year: 2016, Page(s): C2
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  • Why Deep Learning Works: A Manifold Disentanglement Perspective

    Publication Year: 2016, Page(s):1997 - 2008
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3528 KB) | HTML iconHTML

    Deep hierarchical representations of the data have been found out to provide better informative features for several machine learning applications. In addition, multilayer neural networks surprisingly tend to achieve better performance when they are subject to an unsupervised pretraining. The booming of deep learning motivates researchers to identify the factors that contribute to its success. One... View full abstract»

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  • Feedback Solution to Optimal Switching Problems With Switching Cost

    Publication Year: 2016, Page(s):2009 - 2019
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1895 KB) | HTML iconHTML

    The problem of optimal switching between nonlinear autonomous subsystems is investigated in this paper where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to utilization of different modes. The mode sequence is unspecified and a switching cos... View full abstract»

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  • Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations

    Publication Year: 2016, Page(s):2020 - 2034
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3137 KB) | HTML iconHTML

    This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle comple... View full abstract»

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  • Classifying Stress From Heart Rate Variability Using Salivary Biomarkers as Reference

    Publication Year: 2016, Page(s):2035 - 2046
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3388 KB) | HTML iconHTML

    An accurate and noninvasive stress assessment from human physiology is a strenuous task. In this paper, a pattern recognition system to learn complex correlates between heart rate variability (HRV) features and salivary stress biomarkers is proposed. Using the Trier social stress test, heart rate and salivary measurements were obtained from volunteers under varying levels of stress induction. Meas... View full abstract»

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  • Space Structure and Clustering of Categorical Data

    Publication Year: 2016, Page(s):2047 - 2059
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6123 KB) | HTML iconHTML

    Learning from categorical data plays a fundamental role in such areas as pattern recognition, machine learning, data mining, and knowledge discovery. To effectively discover the group structure inherent in a set of categorical objects, many categorical clustering algorithms have been developed in the literature, among which k-modes-type algorithms are very representative because of their good perf... View full abstract»

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  • Kohonen’s Map Approach for the Belief Mass Modeling

    Publication Year: 2016, Page(s):2060 - 2071
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6700 KB) | HTML iconHTML

    In the framework of the evidence theory, several approaches for estimating belief functions are proposed. However, they generally suffer from the problem of masses attribution in the case of compound hypotheses that lose much conceptual contribution of the theory. In this paper, an original method for estimating mass functions using Kohonen's map derived from the initial feature space and an initi... View full abstract»

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  • Multiple Ordinal Regression by Maximizing the Sum of Margins

    Publication Year: 2016, Page(s):2072 - 2083
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2074 KB) | HTML iconHTML

    Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a support vector machine algorithm. Specifically, the goal is to learn a set of classifiers with common directio... View full abstract»

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  • Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller

    Publication Year: 2016, Page(s):2084 - 2094
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2373 KB) | HTML iconHTML

    This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy... View full abstract»

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  • Directional Clustering Through Matrix Factorization

    Publication Year: 2016, Page(s):2095 - 2107
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1599 KB) | HTML iconHTML

    This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction. This directional distance measure arises in several applications, including document classification and human brain imaging. Using ideas from the field of constrained low-rank matrix... View full abstract»

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  • Learning With Jensen-Tsallis Kernels

    Publication Year: 2016, Page(s):2108 - 2119
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2128 KB) | HTML iconHTML

    Jensen-type [Jensen-Shannon (JS) and Jensen-Tsallis] kernels were first proposed by Martins et al. (2009). These kernels are based on JS divergences that originated in the information theory. In this paper, we extend the Jensen-type kernels on probability measures to define positive-definite kernels on Euclidean space. We show that the special cases of these kernels include dot-product kernels. Si... View full abstract»

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  • Tensor LRR and Sparse Coding-Based Subspace Clustering

    Publication Year: 2016, Page(s):2120 - 2133
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4255 KB) | HTML iconHTML

    Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dime... View full abstract»

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  • Model-Free Optimal Tracking Control via Critic-Only Q-Learning

    Publication Year: 2016, Page(s):2134 - 2144
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1466 KB) | HTML iconHTML

    Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine nonlinear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solv... View full abstract»

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  • IEEE Computational Intelligence Society Information

    Publication Year: 2016, Page(s): C3
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  • IEEE Transactions on Neural Networks information for authors

    Publication Year: 2016, Page(s): C4
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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