Kernel Parameter Optimization for Kriging Based on Structural Risk Minimization Principle

Su, Hua; Gong, Chunlin; Gu, Liangxian

doi:10.1155/2017/3021950

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Therefore, RELM works on the SRM principle which is based on the statistical learning theory. It provides the relationship between empirical risk and real risk, which is known as the bound of the generalization ability [ 63 ]. Here, the empirical risk is represented by the sum of squared error, i.e., ‖ ɛ ‖ 2 and structural risk can be represented with ‖ β ‖ 2 .…”

Section: Proposed Methodologymentioning

confidence: 99%

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Bhatti

Jamil

Nida

et al. 2021

Computational Intelligence and Neuroscience

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Classroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor’s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.

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Section: Proposed Methodologymentioning

confidence: 99%

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Bhatti

Jamil

Nida

et al. 2021

Computational Intelligence and Neuroscience

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“…X c is the sum of the N-CWPIC. SVM is a machine learning method based on statistical learning theory and structural risk minimization [14][15][16]. The algorithm essentially finds a maximum-margin hyper-plane (in a three-dimensional space, its hyper-plane is a two-dimensional plane) to maximize the distance between the hyper-plane and the nearest data point.…”

Section: Cwpic Extraction Of Arc Faultsmentioning

confidence: 99%

An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

Yang¹,

Fang²,

Zhang³

et al. 2018

R. Soc. open sci.

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Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.

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Novel kernel density estimator based on ensemble unbiased cross-validation

Huang

et al. 2021

Information Sciences

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Kernel Parameter Optimization for Kriging Based on Structural Risk Minimization Principle

Cited by 3 publications

References 19 publications

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

Novel kernel density estimator based on ensemble unbiased cross-validation

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