A novel learning model-Kernel Granular Support Vector Machine

Guo, Husheng; Wang, Wenjian; Men, Changqian

doi:10.1109/icmlc.2009.5212413

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…There are three main types of Machine Learning [60]- [66] represented in figure 7: In supervised learning, the algorithms are based on already categorized datasets, in order to understand the criteria used for classification and reproducing them [67]- [70]. In unsupervised learning, algorithms are trained from raw data, from which they try to extract patterns [71]- [76]. Finally, in reinforcement learning, the algorithm functions as an autonomous agent, which observes its environment and learns as it interacts with it [73], [75].…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend. The fields of application and potential uses of Artificial Intelligence are increasingly diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine learning, etc. This paper is a state of the art on the classification of radar signals. It focuses on the contribution of artificial intelligence to the latter without forgetting target tracking. This by evoking the different feature extractors, classifiers and the existing identification deep learning algorithms. We detail also the process allowing carrying out this classification.

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Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…SVM is widely used in text classification [33,42], marketing, pattern recognition, and medical diagnosis [43]. A lot of further research is done, GSVM (granular support vector machines) [44][45][46], FSVM (fuzzy support vector machines) [47][48][49], TWSVMs (twin support vector machines) [50][51][52], VaR-SVM (value-at-risk support vector machines) [53], and RSVM (ranking support vector machines) [54]. [55] divide data into meaningful groups (see Figure 3) so that patterns in the same group are similar in some sense and patterns in different group are dissimilar in the same sense.…”

Section: Classificationmentioning

confidence: 99%

Data Mining for the Internet of Things: Literature Review and Challenges

Chen

Deng

Wan

et al. 2015

International Journal of Distributed Sensor Networks

404

215

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The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

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“…Therefore, data distribution errors are inevitable. These two aspects may reduce the generalization ability of GSVM [17].…”

Section: Introductionmentioning

confidence: 96%

“…It has considered two geometric aspects simultaneously, the first is the distance between samples and the best approximate hyperplane and the second is the distance between the best approximate hyperplane and the obtained hyperplane. Considering the difference of granulation on kernel space and original space, an GSVM model based on kernel space is proposed by Guo et al [17], and the rules of granulation on kernel space was given through geometric analysis. However, these approaches may be not effective for some datasets, where the distance between data cannot be measured by European distance.…”

Section: Introductionmentioning

confidence: 99%