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https://dl.acm.org/citation.cfm?id=1014089
Incorporating prior knowledge with weighted margin support vector machines. Full Text: PDF Get this ... We discuss the issues of incorporating prior knowledge using this rather general formulation. ... Qing Wang, Feature weighted confidence to incorporate prior knowledge into support vector machines for classification, Knowledge and Information ...Cited by: 185
https://www.researchgate.net/publication/221653619_Incorporating_prior_knowledge_with_weighted_margin_support_vector_machines
Weighted Margin Support Vector Machines (WMSVM) is another way to solve the small-sampling problem by generalizing the original Support Vector Machines for incorporating prior knowledge.
https://link.springer.com/article/10.1007/s10994-007-5035-5
Nov 17, 2007 · Incorporating prior knowledge with weighted margin support vector machines. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 326–333), Seatle WA, USA.Cited by: 86
https://hal.archives-ouvertes.fr/docs/00/14/30/34/PDF/LauerBlochNeurocomputing07.pdf
For classification, support vector machines (SVMs) have recently been introduced and quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many ap-plications. This paper gives a review of the current state of research regarding the
https://link.springer.com/article/10.1007%2Fs10115-018-1165-2
Feb 10, 2018 · Abstract This paper proposes an approach called feature weighted confidence with support vector machine (FWC–SVM) to incorporate prior knowledge into SVM with sample confidence. First, we use prior features to express prior knowledge.Cited by: 1
https://www.semanticscholar.org/paper/Incorporating-prior-knowledge-with-weighted-margin-Wu-Srihari/4cd2ffebffd60708fb958c82b5fc72813fb9a1dd
Incorporating prior knowledge with weighted margin support vector machines Semantic Scholar Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus a larger training dataset is required for better performance.
https://www.researchgate.net/publication/221497736_Learning_with_Rigorous_Support_Vector_Machines
Learning with Rigorous Support Vector Machines. ... Incorporating prior knowledge with weighted margin support vector machines. ... that permits the incorporation of prior knowledge…
https://www.sciencedirect.com/science/article/pii/S0925231207001439
For classification, support vector machines (SVMs) have recently been introduced and quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications.Cited by: 160
https://papers.nips.cc/paper/2222-knowledge-based-support-vector-machine-classifiers.pdf
Support vector machines (SVMs) have played a major role in classification problems [18,3, 11]. However unlike other classification tools such as knowledge-based neural networks [16, 17, 7], little work [15] has gone into incorporating prior knowledge into support vector machines. In this work we present a novel approach to incorporating
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.375.2184
In this paper, we propose a simple generalization of SVM: Weighted Margin SVM (WMSVMs) that permits the incorporation of prior knowledge. We show that Sequential Minimal Optimization can be used in training WMSVM. We discuss the issues of incorporating prior knowledge using this rather general formulation.
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