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# IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 22 of 22

Publication Year: 2015, Page(s): C1
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• ### IEEE Transactions on Neural Networks and Learning Systems publication information

Publication Year: 2015, Page(s): C2
| PDF (137 KB)
• ### Self-Organizing Neural Networks Integrating Domain Knowledge and Reinforcement Learning

Publication Year: 2015, Page(s):889 - 902
Cited by:  Papers (14)
| | PDF (2496 KB) | HTML

The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforcement learning (RL), domain knowledge cannot be inserted directly. In this paper, we show how self-organizing neural networks designed for online and i... View full abstract»

• ### Variable Neural Adaptive Robust Control: A Switched System Approach

Publication Year: 2015, Page(s):903 - 915
Cited by:  Papers (8)
| | PDF (2227 KB) | HTML

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs accordi... View full abstract»

• ### Integral Reinforcement Learning for Continuous-Time Input-Affine Nonlinear Systems With Simultaneous Invariant Explorations

Publication Year: 2015, Page(s):916 - 932
Cited by:  Papers (27)
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This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal ca... View full abstract»

• ### Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

Publication Year: 2015, Page(s):933 - 950
Cited by:  Papers (17)
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Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data s... View full abstract»

• ### Graph Embedded Nonparametric Mutual Information for Supervised Dimensionality Reduction

Publication Year: 2015, Page(s):951 - 963
Cited by:  Papers (13)
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In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their corresponding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Furthermore, recent quadratic nonparametric implementations of MI are computationally efficient and do not require any prio... View full abstract»

• ### The Minimum Risk Principle That Underlies the Criteria of Bounded Component Analysis

Publication Year: 2015, Page(s):964 - 981
Cited by:  Papers (4)
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This paper studies the problem of the blind extraction of a subset of bounded component signals from the observations of a linear mixture. In the first part of this paper, we analyze the geometric assumptions of the observations that characterize the problem, and their implications on the mixing matrix and latent sources. In the second part, we solve the problem by adopting the principle of minimi... View full abstract»

• ### A One-Class Kernel Fisher Criterion for Outlier Detection

Publication Year: 2015, Page(s):982 - 994
Cited by:  Papers (8)
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Recently, Dufrenois and Noyer proposed a one class Fisher's linear discriminant to isolate normal data from outliers. In this paper, a kernelized version of their criterion is presented. Originally on the basis of an iterative optimization process, alternating between subspace selection and clustering, I show here that their criterion has an upper bound making these two problems independent. In pa... View full abstract»

• ### Semi-Supervised Nearest Mean Classification Through a Constrained Log-Likelihood

Publication Year: 2015, Page(s):995 - 1006
Cited by:  Papers (15)
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We cast a semi-supervised nearest mean classifier, previously introduced by the first author, in a more principled log-likelihood formulation that is subject to constraints. This, in turn, leads us to make the important suggestion to not only investigate error rates of semi-supervised learners but also consider the risk they originally aim to optimize. We demonstrate empirically that in terms of c... View full abstract»

• ### Adaptive NN Controller Design for a Class of Nonlinear MIMO Discrete-Time Systems

Publication Year: 2015, Page(s):1007 - 1018
Cited by:  Papers (101)
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An adaptive neural network tracking control is studied for a class of multiple-input multiple-output (MIMO) nonlinear systems. The studied systems are in discrete-time form and the discretized dead-zone inputs are considered. In addition, the studied MIMO systems are composed of $N$ subsystems, and each subsystem contains unknown functions and external disturbance. Due to the complicated framework... View full abstract»

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

Publication Year: 2015, Page(s):1019 - 1034
Cited by:  Papers (84)
| | PDF (3183 KB) | HTML

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»

• ### Robust Sensorimotor Representation to Physical Interaction Changes in Humanoid Motion Learning

Publication Year: 2015, Page(s):1035 - 1047
Cited by:  Papers (5)
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This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply this knowledge during different physical interactions between a robot and its surroundings. The phase transfer sequence represents the temporal order of... View full abstract»

• ### A New Method for Data Stream Mining Based on the Misclassification Error

Publication Year: 2015, Page(s):1048 - 1059
Cited by:  Papers (40)
| | PDF (2643 KB) | HTML

In this paper, a new method for constructing decision trees for stream data is proposed. First a new splitting criterion based on the misclassification error is derived. A theorem is proven showing that the best attribute computed in considered node according to the available data sample is the same, with some high probability, as the attribute derived from the whole infinite data stream. Next thi... View full abstract»

• ### Learning to Track Multiple Targets

Publication Year: 2015, Page(s):1060 - 1073
Cited by:  Papers (10)
| | PDF (2548 KB) | HTML

Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algorithm, we learn that a discriminative structure prediction model from labeled video data captures the interdependence of multiple influence factors. G... View full abstract»

• ### Adaptive Neural Control of Nonlinear MIMO Systems With Time-Varying Output Constraints

Publication Year: 2015, Page(s):1074 - 1085
Cited by:  Papers (46)
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In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is s... View full abstract»

• ### Very Sparse LSSVM Reductions for Large-Scale Data

Publication Year: 2015, Page(s):1086 - 1097
Cited by:  Papers (25)
| | PDF (3182 KB) | HTML

Least squares support vector machines (LSSVMs) have been widely applied for classification and regression with comparable performance with SVMs. The LSSVM model lacks sparsity and is unable to handle large-scale data due to computational and memory constraints. A primal fixed-size LSSVM (PFS-LSSVM) introduce sparsity using Nyström approximation with a set of prototype vectors (PVs). The PFS-LSSVM ... View full abstract»

• ### Sparse Multivariate Gaussian Mixture Regression

Publication Year: 2015, Page(s):1098 - 1108
Cited by:  Papers (1)
| | PDF (1656 KB) | HTML

Fitting a multivariate Gaussian mixture to data represents an attractive, as well as challenging problem, in especial when sparsity in the solution is demanded. Achieving this objective requires the concurrent update of all parameters (weight, centers, and precisions) of all multivariate Gaussian functions during the learning process. Such is the focus of this paper, which presents a novel method ... View full abstract»

• ### Variational Inference With ARD Prior for NIRS Diffuse Optical Tomography

Publication Year: 2015, Page(s):1109 - 1114
| | PDF (904 KB) | HTML

Diffuse optical tomography (DOT) reconstructs 3-D tomographic images of brain activities from observations by near-infrared spectroscopy (NIRS) that is formulated as an ill-posed inverse problem. This brief presents a method for NIRS DOT based on a hierarchical Bayesian approach introducing the automatic relevance determination prior and the variational Bayes technique. Although the sparseness of ... View full abstract»

• ### An Efficient Topological Distance-Based Tree Kernel

Publication Year: 2015, Page(s):1115 - 1120
Cited by:  Papers (3)
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Tree kernels proposed in the literature rarely use information about the relative location of the substructures within a tree. As this type of information is orthogonal to the one commonly exploited by tree kernels, the two can be combined to enhance state-of-the-art accuracy of tree kernels. In this brief, our attention is focused on subtree kernels. We describe an efficient algorithm for injecti... View full abstract»

• ### IEEE Computational Intelligence Society Information

Publication Year: 2015, Page(s): C3
| PDF (116 KB)
• ### IEEE Transactions on Neural Networks information for authors

Publication Year: 2015, Page(s): C4
| PDF (124 KB)

## 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