Fast Gaussian kernel learning for classification tasks based on specially structured global optimization

Zhong, Shangping; Chen, Tianshun; He, Fazhi; Niu, Yuzhen

doi:10.1016/j.neunet.2014.05.014

Cited by 36 publications

(12 citation statements)

References 19 publications

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“…(1) RFID Intelligent Theft-Proof System: The RFID technology used in anti-theft door proceeds with the automatic identification only for a single item; when the customer tries to bring the unpaid products out of the outlet, the RFID intelligent anti-theft system will automatically alert and notify sales staff of which items have been illegally brought out of the outlet. [46] [47] (2) RFID Intelligent POS System: The RFID intelligent POS system can simultaneously support the RFID technology and traditional bar code to proceed with the quick checkout process, and accurately scan multiple items to significantly reduce customers' waiting time, which not only improves customer satisfaction but also avoids missing the sales opportunities during the peak shopping hours. [48] (3) RFID Stocktaking System: The RFID handheld device makes a quick scan on the product tag to enhance the stocktaking efficiency for the outlet; through the RFID handheld device, the outlet people can read the tag information on the product to retrieve the detailed product information (including: tile, specification, price, etc.…”

Section: Figure 3 the Rfid Interactive Mechanismmentioning

confidence: 99%

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Yang¹,

Chen

et al. 2021

IEEE Access

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Section: Figure 3 the Rfid Interactive Mechanismmentioning

confidence: 99%

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Yang¹,

Chen

et al. 2021

IEEE Access

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“…The essence of RF is an extension of Bagging that each decision tree is a classifier and for an input sample [3,11], N trees will have N classification results. RF integrates all classification voting results and specifies the category with the most votes as the final output [49]. , so as to separate the data of different categories.…”

Section: Experimental Settingmentioning

confidence: 99%

Unsupervised Text Feature Learning via Deep Variational Auto-encoder

Liu

Xie

Chen

2020

ITC

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Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. The dimensionality reduction methods can be applied to all types of data, especially image data. Although the supervised learning method has achieved good results in the application of dimensionality reduction, its performance depends on the number of labeled training samples. With the growing of information from internet, marking the data requires more resources and is more difficult. Therefore, using unsupervised learning to learn the feature of data has extremely important research value. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE), and the method can be significantly improved over other comparison methods.

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“…Huang et al propose a computationally efficient method 31 for traffic sign recognition which is combined with an extreme learning machine algorithm classifier. Zhong et al present a fast Gaussian kernel learning method 32 and it has been shown to converge a global solution for any classified task.…”

Section: Related Workmentioning

confidence: 99%

Detection and defense against network isolation attacks in software‐defined networks

Zhu

Xiao

et al. 2020

Trans Emerging Tel Tech

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With the development and pervasiveness of Internet of Things (IoT) devices, Software‐Defined Networks (SDN) technology has been deployed to bring great convenience to network transmission. However, SDN over IoT network still faces many challenges on devices data security. Our work demonstrates a novel attack of SDN networks, named Network Harvesting (NH). In NH, an attacker has the ability to steal the users' network privileges without the awareness of victims and the switchers. Furthermore, to solve the above attack, we construct a detection scheme and a defense scheme, named RSDetector and SpoofDefender. RSDetector detects the presence of rogue switches in the network by leveraging the prediction power of machine learning. At the same time, SpoofDefender prevents a number of spoofing attacks including NH by the global control of the SDN networks. In addition, RSDetector and SpoofDefender are also evaluated on ONOS 1.10.4 and Mininet. A good deal of simulation results demonstrate that our proposed schemes have great optimization in reducing communication and computation costs.

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Fast Gaussian kernel learning for classification tasks based on specially structured global optimization

Cited by 36 publications

References 19 publications

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Intelligent Agent-Based Predict System With Cloud Computing for Enterprise Service Platform in IoT Environment

Unsupervised Text Feature Learning via Deep Variational Auto-encoder

Detection and defense against network isolation attacks in software‐defined networks

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