An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function

Gupta, Umesh C.; Gupta, Deepak

doi:10.1007/s10489-019-01465-w

Cited by 32 publications

(7 citation statements)

References 44 publications

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“…Digitization is the foundation of IoT, including the procurement of medical laboratory equipment, reagents and consumables, technical outsourcing service customization, to intermediate links such as consultation, appointment, diagnosis, treatment, medication and rehabilitation, to the delivery of final products, services, and medical insurance [14]. At present, the degree of digitalization of the supply-side and demand-side enterprise management of medical elements is relatively low, which is a key transformation link of the medical industry chain [15][16]. The industrial Internet of Things platform will be an important driver of digitalization, and gradually realize the digitalization of procurement, digitalization of production, digitalization of management, digitalization of marketing, and transactions.…”

Section: Digitalization Of Medical Industrymentioning

confidence: 99%

Development Status and Trend Analysis of Internet of Medical Things Industry in China

Jiao

2022

Modern Management Based on Big Data III

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This article analyzes the development status, development trend and prospects of China’s Internet of Medical Things (IoMT) industry from a macro perspective. Our survey mainly includes: analyzing the necessity and urgency of China’s medical system reform from the various dilemmas faced by China’s medical system, and analyzing the development of the IoMT industry based on the current basic conditions of development of the Internet of Things (IoT), information technology and background of COVID-19 epidemic. Opportunities and the evolution of China’s IoMT policy were also analyzed. Moreover, from the five aspects of medical industry informatization, Internet hospitals, smart wearable devices, medical AI industry and medical industry digitization, the development status and trends of China’s IoMT industry are analyzed. Finally, it looks forward to the development prospects and directions of IoMT industry for health care in China.

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Section: Digitalization Of Medical Industrymentioning

confidence: 99%

Development Status and Trend Analysis of Internet of Medical Things Industry in China

Jiao

2022

Modern Management Based on Big Data III

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“…This section present the theory behind SVR equations as given by [27], [28]. Given a regression problem as (1).…”

Section: Theoretical Concept 21 Support Vector Regression (Svr)mentioning

confidence: 99%

“…Support vector machines (SVM) [1]- [7] as a statistical learning theory based machine learning technique introduced [6], [8] has gained popularity in both classification and regression tasks due to its various associated prominent abilities that led to a promising generalization performance [6], [9]. Some of the most distinguished features of SVR include its ability to model non-linear relationship among data samples, non-dependence on input space dimensionality for its generalization and always resulting to global solution due to its quadratic formulation approach.…”

Section: Introductionmentioning

confidence: 99%

Determination of support vector regression parameters using African buffalo optimization algorithm

Maijama’a

Yusof

Mohsin

2022

IJEECS

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<span lang="EN-US">The use of support vector regression (SVR) for regression tasks has been on increase over the past few years. Unfortunately, the practical application of SVR for regression task is limited due to its dependence on proper setting of its hyper-parameters and associated kernel parameter. Therefore, it become imperative to device a reliable and fast mechanism of determining the value of these parameters that could guarantee lowest generalization error. This paper presents SVR parameter optimization approaches using African buffalo optimisation (ABO) algorithm, i.e. SVR-ABO. The SVR parameters are optimized by using African buffalo optimisation algorithm. Results obtained from several experiments performed has shown that the proposed ABO algorithm has the capability of determining SVR hyper-parameters which most of time has to be done through estimation.</span>

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“…For example, for efectively reducing the impact of noise in the dataset, solving the noise sensitivity and instability of resampling, realizing the highprecision classifcation of imbalanced data, and the efcient classifcation of dynamically obtained data or stream data, such as the research of its extended algorithms has always been one of the research directions in the feld of machine learning. Tese studies are of great signifcance for support vector machines and their variants [7][8][9][10]. In these new topics, the models evolved from SVM inherit most of the original characteristics, such as interval theory, kernel techniques, and structural risk minimization and also inherit the defects of the original SVM model.…”

Section: Introductionmentioning

confidence: 99%

An ISVM Algorithm Based on High-Dimensional Distance and Forgetting Characteristics

Xie

et al. 2022

Scientific Programming

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In the face of the batch, dynamic access data, or the flow of data that continuous changes over time, the traditional support vector machine algorithm cannot dynamically adjust the previous classification model. To overcome this shortcoming, the incremental support vector machine (ISVM) algorithm is proposed. However, many incremental support vector algorithms still have shortcomings such as low efficiency, memory limitation, and poor generalization. This paper puts forward the new ISVM algorithm, HDFC-ISVM ∗ algorithm, based on the high-dimensional distance and forgetting characteristics. This paper firstly proposes the original HDFC-ISVM algorithm that first learns the distribution characteristics of the samples according to the distance between the samples and the normative hyperplane. Then, it introduces the forgetting factor. In the incremental learning process, the classifier gradually accumulates the spatial distribution knowledge of samples, eliminates the samples that have no contributions to the classifier, and selectively forgets some useless samples according to the forgetting factor, which overcomes the shortcomings such as low efficiency and poor accuracy of some algorithms. But, the original HDFC-ISVM algorithm is sensitive to parameters, and different settings of the parameters have a great impact on the final classification accuracy of the algorithm. Therefore, on the basis of the original algorithm, an improved algorithm HDFC-ISVM ∗ based on the adjustments to the initialization strategy and updating rules of the forgetting factor is proposed. The initialization strategy and updating rules of the forgetting factor are adjusted to adapt datasets with different distributions in this improved algorithm. The rationality of the improved strategy about the forgetting factor is discussed theoretically. At the same time, the proposed algorithm has better classification accuracy, classification efficiency, and better generalization ability than other algorithms, which is verified by experiments.

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An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function

Cited by 32 publications

References 44 publications

Development Status and Trend Analysis of Internet of Medical Things Industry in China

Development Status and Trend Analysis of Internet of Medical Things Industry in China

Determination of support vector regression parameters using African buffalo optimization algorithm

An ISVM Algorithm Based on High-Dimensional Distance and Forgetting Characteristics

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