# IEEE Transactions on Cybernetics

## Filter Results

Displaying Results 1 - 18 of 18

Publication Year: 2014, Page(s): C1
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• ### IEEE Transactions on Cybernetics publication information

Publication Year: 2014, Page(s): C2
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• ### A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences

Publication Year: 2014, Page(s):569 - 582
Cited by:  Papers (13)
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Periodic pattern detection in time-ordered sequences is an important data mining task, which discovers in the time series all patterns that exhibit temporal regularities. Periodic pattern mining has a large number of applications in real life; it helps understanding the regular trend of the data along time, and enables the forecast and prediction of future events. An interesting related and vital ... View full abstract»

• ### Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems

Publication Year: 2014, Page(s):583 - 593
Cited by:  Papers (166)
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This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the ... View full abstract»

• ### Principal Component Analysis by$L_{p}$-Norm Maximization

Publication Year: 2014, Page(s):594 - 609
Cited by:  Papers (35)
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This paper proposes several principal component analysis (PCA) methods based on Lp-norm optimization techniques. In doing so, the objective function is defined using the Lp-norm with an arbitrary p value, and the gradient of the objective function is computed on the basis of the fact that the number of training samples is finite. In the first part, an easier problem of extrac... View full abstract»

• ### Approximation-Based Adaptive Neural Control Design for a Class of Nonlinear Systems

Publication Year: 2014, Page(s):610 - 619
Cited by:  Papers (38)
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This paper focuses on approximation-based adaptive neural control of a class of nonlinear non-strict-feedback systems. Based on the structural characteristic and the monotonously increasing property of the system bounding functions, a variable separation method is first developed. By this method, an approximation-based adaptive backstepping approach is proposed for a class of nonlinear non-strict-... View full abstract»

• ### A New Approach to Classifier Fusion Based on Upper Integral

Publication Year: 2014, Page(s):620 - 635
Cited by:  Papers (27)
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Fusing a number of classifiers can generally improve the performance of individual classifiers, and the fuzzy integral, which can clearly express the interaction among the individual classifiers, has been acknowledged as an effective tool of fusion. In order to make the best use of the individual classifiers and their combinations, we propose in this paper a new scheme of classifier fusion based o... View full abstract»

• ### Constraint Neighborhood Projections for Semi-Supervised Clustering

Publication Year: 2014, Page(s):636 - 643
Cited by:  Papers (10)
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Semi-supervised clustering aims to incorporate the known prior knowledge into the clustering algorithm. Pairwise constraints and constraint projections are two popular techniques in semi-supervised clustering. However, both of them only consider the given constraints and do not consider the neighbors around the data points constrained by the constraints. This paper presents a new technique by util... View full abstract»

• ### Symmetry Constraint for Foreground Extraction

Publication Year: 2014, Page(s):644 - 654
Cited by:  Papers (4)
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Symmetry as an intrinsic shape property is often observed in natural objects. In this paper, we discuss how explicitly taking into account the symmetry constraint can enhance the quality of foreground object extraction. In our method, a symmetry foreground map is used to represent the symmetry structure of the image, which includes the symmetry matching magnitude and the foreground location prior.... View full abstract»

• ### PSO-MISMO Modeling Strategy for MultiStep-Ahead Time Series Prediction

Publication Year: 2014, Page(s):655 - 668
Cited by:  Papers (29)
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Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dom... View full abstract»

• ### Image Annotation by Multiple-Instance Learning With Discriminative Feature Mapping and Selection

Publication Year: 2014, Page(s):669 - 680
Cited by:  Papers (90)
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Multiple-instance learning (MIL) has been widely investigated in image annotation for its capability of exploring region-level visual information of images. Recent studies show that, by performing feature mapping, MIL can be cast to a single-instance learning problem and, thus, can be solved by traditional supervised learning methods. However, the approaches for feature mapping usually overlook th... View full abstract»

• ### Projection-Based Ensemble Learning for Ordinal Regression

Publication Year: 2014, Page(s):681 - 694
Cited by:  Papers (23)
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The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper proposes an ensemble methodology specifically adapted to this type of problem, which is based on computing different classification tasks through the formulation of different order hypotheses. Every single model is trained in order to distinguish between one given class (k) and all the rem... View full abstract»

• ### Discriminative BoW Framework for Mobile Landmark Recognition

Publication Year: 2014, Page(s):695 - 706
Cited by:  Papers (18)
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This paper proposes a new soft bag-of-words (BoW) method for mobile landmark recognition based on discriminative learning of image patches. Conventional BoW methods often consider the patches/regions in the images as equally important for learning. Amongst the few existing works that consider the discriminative information of the patches, they mainly focus on selecting the representative patches f... View full abstract»

• ### An Efficient Variable Projection Formulation for Separable Nonlinear Least Squares Problems

Publication Year: 2014, Page(s):707 - 711
Cited by:  Papers (10)
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We consider in this paper a class of nonlinear least squares problems in which the model can be represented as a linear combination of nonlinear functions. The variable projection algorithm projects the linear parameters out of the problem, leaving the nonlinear least squares problems involving only the nonlinear parameters. To implement the variable projection algorithm more efficiently, we propo... View full abstract»

• ### Multiscale Asymmetric Orthogonal Wavelet Kernel for Linear Programming Support Vector Learning and Nonlinear Dynamic Systems Identification

Publication Year: 2014, Page(s):712 - 724
Cited by:  Papers (5)
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Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the c... View full abstract»

• ### Minimizing Illumination Differences for 3D to 2D Face Recognition Using Lighting Maps

Publication Year: 2014, Page(s):725 - 736
Cited by:  Papers (9)  |  Patents (1)
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Asymmetric 3D to 2D face recognition has gained attention from the research community since the real-world application of 3D to 3D recognition is limited by the unavailability of inexpensive 3D data acquisition equipment. A 3D to 2D face recognition system explicitly relies on 3D facial data to account for uncontrolled image conditions related to head pose or illumination. We build upon such a sys... View full abstract»

• ### IEEE Systems, Man, and Cybernetics Society Information

Publication Year: 2014, Page(s): C3
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• ### IEEE Transactions on Cybernetics information for authors

Publication Year: 2014, Page(s): C4
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## Aims & Scope

The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Prof. Jun Wang
Dept. of Computer Science
City University of Hong Kong
Kowloon Tong, Kowloon, Hong Kong
Tel: +852 34429701
Email: jwang.cs@cityu.edu.hk