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https://link.springer.com/chapter/10.1007/978-3-642-01510-6_40
Abstract. Recently, Support Vector Machines (SVMs) have been applied very effectively in learning ranking functions (or preference functions).They intend to learn ranking functions with the principles of the large margin and the kernel trick.However, the output of a ranking function is a score function which is not a calibrated posterior probability to enable post-processing.Cited by: 6
https://www.researchgate.net/publication/220871766_Probabilistic_Ranking_Support_Vector_Machine
In order to represent the effect of each feature on the log odds ratio on the nomograms, we use probabilistic ranking support vector machines which map the support vector machine outputs into a ...
https://www.researchgate.net/post/Does_support_vector_machine_offer_a_probabilistic_interpretation
Does support vector machine offer a probabilistic interpretation? Is it possible to use Ranking_SVM to obtain probabilistic interpretation? ... Can anyone explain to me hard and soft margin ...
https://www.sciencedirect.com/science/article/pii/S0020025513003678
The proposed method utilizes multi-support vector domain description (multi-SVDD) to construct pseudo-conditional probabilities for concordant data pairs and provides decision options for ranking pairs by controlling the thresholds of estimated probability. Simulations results denote that the proposed method efficiently captures underlying ...Cited by: 9
https://en.wikipedia.org/wiki/Probabilistic_classification
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles
https://www.sciencedirect.com/science/article/pii/S0950705112000883
Their models promote the applications of support vector machines , , , and have been extended to other binary SVM-type methods and multi-class SVM methods , . In this paper, we are concerned about the probability estimates for twin support vector machine (TWSVM) .Cited by: 36
https://link.springer.com/chapter/10.1007%2F978-3-642-21090-7_11
In order to represent the effect of each feature on the log odds ratio on the nomograms, we use probabilistic ranking support vector machines which map the support vector machine outputs into a probabilistic sigmoid function whose parameters are trained by using cross-validation.Cited by: 1
https://dl.acm.org/citation.cfm?id=299099
Nguyen Thi Thuy , Ngo Anh Vien , Nguyen Hoang Viet , Taechoong Chung, Probabilistic Ranking Support Vector Machine, Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II, May 26-29, 2009, Wuhan, ChinaCited by: 318
http://www.cs.cornell.edu/courses/cs4300/2013fa/lectures/learning-to-rank-4pp.pdf
Information Retrieval INFO 4300 / CS 4300 ! Retrieval models – Older models » Boolean retrieval » Vector Space model – Probabilistic Models » BM25 » Language models – Web search » Learning to Rank Generative vs. Discriminative ! All of the probabilistic retrieval models presented ... – e.g. Ranking Support Vector Machine (SVM ...
https://scikit-learn.org/stable/modules/svm.html
Support Vector Machine algorithms are not scale invariant, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it to have mean 0 and variance 1. Note that the same scaling must be applied to
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