classyfire package is to improve the quality of multivariate classification projects by making a state-of-the-art multivariate classification workflow available to everyone. Classyfire achieves this by providing powerful functions which automate as much of the classifier building and testing as possible. However, to avoid these functions becoming impenetrable black boxes, detailed information is provided about how these functions work, and full access is provided to the internals of all classifiers that are produced.
| Package: |
| classyfire |
| Type: |
| Package |
| Version: |
| 0.1-2 |
| Date: |
| 2015-01-11 |
| License: |
| GPL (>= 2) |
Chang, Chih-Chung and Lin, Chih-Jen: LIBSVM: a library for Support Vector Machines http://www.csie.ntu.edu.tw/~cjlin/libsvm
Exact formulations of models, algorithms, etc. can be found in the document: Chang, Chih-Chung and Lin, Chih-Jen: LIBSVM: a library for Support Vector Machines http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz
More implementation details and speed benchmarks can be found on: Rong-En Fan and Pai-Hsune Chen and Chih-Jen Lin: Working Set Selection Using the Second Order Information for Training SVM http://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf
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Booth, J.G., Hall, P. and Wood, A.T.A. Balanced importance resampling for the bootstrap. Annals of Statistics, 21, 286-298, 1993 Davison, A.C. and Hinkley, D.V. Bootstrap Methods and Their Application Cambridge University Press, 1997
Efron, B. and Tibshirani, R. An Introduction to the Bootstrap Chapman & Hall, 1993