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https://link.springer.com/article/10.1007%2Fs10462-017-9555-5
Apr 19, 2017 · Granular support vector machine (GSVM) is a novel machine learning model based on granular computing and statistical learning theory, and it can solve the low efficiency learning problem that exists in the traditional SVM and obtain satisfactory generalization performance, as well.Cited by: 5
https://www.sciencedirect.com/science/article/pii/S0933365705000540
Methodology: A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems.Cited by: 85
https://www.researchgate.net/publication/251354892_Research_of_granular_support_vector_machine
Granular support vector machine (GSVM) is a new learning model based on Granular Computing and Statistical Learning Theory. Compared with the traditional SVM, GSVM improves the generalization ...
https://pdfs.semanticscholar.org/b92e/5cf1e2708ab7d86d9ee930417affbb7a356d.pdf
GRANULAR SUPPORT VECTOR MACHINES BASED ON GRANULAR COMPUTING, SOFT COMPUTING AND STATISTICAL LEARNING by YUCHUN TANG Under the Direction of Yan-Qing Zhang ABSTRACT With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems.
https://www.researchgate.net/publication/257352232_Granular_support_vector_machine_based_on_mixed_measure
Granular support vector machines systematically and formally combines the principles from statistical learning theory and granular computing theory. It works by building a sequence of information ...
https://core.ac.uk/display/71421692
In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical learning theory, granular computing theory and soft computing theory to address challenging predictive data modeling problems effectively and/or efficiently, with specific focus on binary ...Author: Yuchun Tang
https://ieeexplore.ieee.org/document/1393935/
Granular support vector machines systematically and formally combines the principles from statistical learning theory and granular computing theory. It works by building a sequence of information granules and then building a support vector machine in each information granule.
https://link.springer.com/chapter/10.1007/978-3-319-22053-6_5
In order to solve this problem, a novel algorithm called Granular Twin Support Vector Machines based on Mixture Kernel Function (GTWSVM-MK) is proposed. Firstly, a grain method including coarse particles and fine particles is propsed and then the judgment and extraction methods of support vector …Cited by: 1
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.2558
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems. In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical …
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
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