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https://research.google/pubs/pub45416/
The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes.Cited by: 197
https://projecteuclid.org/download/pdfview_1/euclid.aos/1205420509
Support Vector Machines with Applications Moguerza, Javier M. and Muñoz, Alberto, Statistical Science, 2006; Improving the Solution of Least Squares Support Vector Machines with Application to a Blast Furnace System Jian, Ling, Shen, Shuqian, and Song, Yunquan, Journal of Applied Mathematics, 2012Cited by: 197
https://en.wikipedia.org/wiki/Support-vector_machine
The support-vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications. [citation needed
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.312.9993
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.324.3859
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes.
https://arxiv.org/abs/0804.0551
The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes. Our main result builds on the observation made by other authors that the SVM can be viewed as a statistical ...Cited by: 197
https://www.academia.edu/13743316/STATISTICAL_PERFORMANCE_OF_SUPPORT_VECTOR_MACHINES
The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a sta- tistical perspective, using tools of concentration
https://ocw.mit.edu/courses/health-sciences-and-technology/hst-951j-medical-decision-support-spring-2003/lecture-notes/lecture12.pdf
• Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998; 2(2):121-167. • Christianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge University Press 2000. • Vapnik V. …
http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf
2 Support Vector Machines: history II Centralized website: www.kernel-machines.org. Several textbooks, e.g. ”An introduction to Support Vector Machines” by Cristianini and Shawe-Taylor is one. A large and diverse community work on them: from machine learning, optimization, statistics, neural networks, functional analysis, etc.
https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf
Support Vector machines can be defined as systems which use hypothesis space of a linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. Support vector machine was initially popular with the NIPS
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