Primate-inspired adaptive routing in intermittently connected mobile communication systems

Sun, Quan; Hu, Fei; Wu, Yue; Huang, Xin-Lin

doi:10.1007/s11276-014-0719-9

Cited by 13 publications

(10 citation statements)

References 45 publications

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“…Generally speaking, The LM scheme collects much more mark information than other schemes, so its overhead will be higher. However, on the other hand, the target of traceback scheme is to collect mark information as much as possible, and it help to locate the malicious node more quickly, so it is necessary to pay the overhead [20][21][22].…”

Section: Resultsmentioning

confidence: 99%

“…The ID number of the node and its private key is assigned to each node before it is deployed [2,10,25]. After the network is deployed, sensor nodes work to collect information from their surroundings and report back to the Sink via multihop wireless channels [2,10,20,25]. The link failure/recovery which result from node failure is not considered in this article [27,28].…”

Section: The System Model and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

A novel joint logging and migrating traceback scheme for achieving low storage requirement and long lifetime in WSNs

Liu

2015

AEU - International Journal of Electronics and Communications

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Section: Resultsmentioning

confidence: 99%

Section: The System Model and Problem Statementmentioning

confidence: 99%

A novel joint logging and migrating traceback scheme for achieving low storage requirement and long lifetime in WSNs

Liu

2015

AEU - International Journal of Electronics and Communications

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“…The experiment results show that the supervised CFA outperforms both linear and kernel versions of CFA without considering class label information. We also plan to explore potential of proposed algorithm using big data analysis and high performance computing [20], [21], [22], [23], [24], [9], [10], [25], [26], [27], [28], [29], [30], [31], [20], for applications of big multimodal data processing [32], [33], [34], [35], [36], [37], information and network security [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], computer vision [48], [49], [50], [51], [52], [53], [54], [55], [56], and bioinformatics [57], [58], [59], [60], [61], [62], [60], [63],…”

Section: Discussionmentioning

confidence: 99%

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

Wang

Zhou

Duan³

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.

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“…The proposed method outperformed the predictor with other popularly used loss functions. In the future, we will investigate if the regularized maximum correntropy framework can be used to regularize ranking score learning [23,24], data representation [25,26,27,28,29,30,31,32,33] Moreover, we also plan to extend the proposed regularized correntropy based classifier for wireless sensor network [34,35,36,37,38,39,40], computer vision [41,42,43,44,45,46,46,47,48], and computer network security [49,50,51,52,53,54,55].…”

Section: Discussionmentioning

confidence: 99%

Regularized maximum correntropy machine

Wang

Jing

et al. 2015

Neurocomputing

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a b s t r a c tIn this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

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Primate-inspired adaptive routing in intermittently connected mobile communication systems

Cited by 13 publications

References 45 publications

A novel joint logging and migrating traceback scheme for achieving low storage requirement and long lifetime in WSNs

A novel joint logging and migrating traceback scheme for achieving low storage requirement and long lifetime in WSNs

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

Regularized maximum correntropy machine

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