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https://people.eecs.berkeley.edu/~jordan/papers/zhang-uai06.pdf
a hierarchical Bayesian architecture and to a fully-Bayesian inference procedure for multi-class classi cation based on data augmenta-tion. We present empirical results that show that the advantages of the Bayesian formal-ism are obtained without a loss in classi ca-tion accuracy. 1 Introduction The support vector machine (SVM) is a popular
https://aapm.onlinelibrary.wiley.com/doi/full/10.1118/1.3352709
Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better.Cited by: 85
https://www.ncbi.nlm.nih.gov/pubmed/20443461
Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Jayasurya K(1), Fung G, Yu S, Dehing-Oberije C, De Ruysscher D, Hope A, De Neve W, Lievens Y, Lambin P, Dekker AL.Cited by: 85
https://link.springer.com/chapter/10.1007%2F978-3-319-71249-9_19
Dec 30, 2017 · We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is...Cited by: 4
https://www.sciencedirect.com/science/article/pii/S089069550200264X
This paper presents a hybrid Support Vector Machines (SVM)–Bayesian Network (BN) model that seeks to address this issue. The experimental data is first classified using a …Cited by: 107
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.
https://dzone.com/articles/introduction-6-machine
Numeric variable is generally not a good fit for Bayesian network. Support Vector Machine It is based on finding a linear plane with maximum margin to separate two class of output.
https://support.sas.com/resources/papers/proceedings17/SAS0474-2017.pdf
Building Bayesian Network Classifiers Using the HPBNET Procedure ... In contrast, support vector machines and neural network classifiers are black boxes and logistic regression and decision tree classifiers only estimate the conditional distribution of the target. Therefore, BN classifiers have great potential in real-world classification ...
https://stats.stackexchange.com/questions/139728/when-to-use-bayesian-networks-over-other-machine-learning-approaches
I expect there may be no definitive answer to this question. But I have used a number of machine learning algorithms in the past and am trying to learn about Bayesian Networks. I would like to understand under what circumstance, or for what types of problems would you choose to use Bayesian Network over other approaches?
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