Searching for Application Computing Fuzziness In Machine Soft Study Support Theory Vector information? Find all needed info by using official links provided below.
https://link.springer.com/book/10.1007/b95439
The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications.
https://www.springer.com/gp/book/9783540243885
The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector
https://www.researchgate.net/publication/246664365_Learning_and_Soft_Computing_Support_Vector_Machines_Neural_Networks_and_Fuzzy_Logic_Models
Support vector machine (SVM) SVM is a soft computing AI method developed by Vapnik (1995). The method has been successfully used in classification and recently in regression (Kecman 2001) .
http://www.ntu.edu.sg/home/ELPWang/PDF_web/05_SVM_basic.pdf
The support vector machine (SVM) is a supervised learning method that generates input-output mapping functions from a set of labeled training data. The mapping function can be either a classification function, i.e., the cate- gory of the input data, or a regression function.
https://en.front-sci.com/index.php/JAI/article/view/40
Fuzzy support vector machines models are developed to implement such an idea. The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law; whereas, independent variables are financial ratios.Author: Guang-Yih Sheu
https://link.springer.com/chapter/10.1007/10984697_7
Apr 22, 2005 · Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ ... Rivieccio F., Sterpi D. Theoretical and Practical Model Selection Methods for Support Vector Classifiers. In: Wang L. (eds) Support Vector Machines: Theory and Applications. Studies in Fuzziness and Soft Computing, vol 177. Springer, Berlin, Heidelberg. First ...
https://www.researchgate.net/publication/221621494_Support_Vector_Machines_Theory_and_Applications
Support Vector Machines (SVM) have been recently developed in the framework of statistical learning theory, and have been successfully applied to a number of applications, ranging from time series prediction, to face recognition, to biological data processing for medical diagnosis.
https://www.scirp.org/journal/PaperInformation.aspx?paperID=27408
Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications.Cited by: 12
https://doi.org/10.3233/IDT-150243
This paper aims at providing the concept of information granulation in Granular computing based pattern classification that is used to deal with incomplete, unreliable, uncertain knowledge from the view of a dataset.Cited by: 1
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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