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- Description:
This library implements various Bayesian linear models (Bayesian linear regression) and generalised linear models. A few features of this library are:
A fancy basis functions/feature composition framework for combining basis functions like radial basis function, sigmoidal basis functions, polynomial basis functions etc.
Basis functions that can be used to approximate Gaussian processes with shift invariant covariance functions (e.g. square exponential) when used with linear models [1], [2], [3].
Non-Gaussian likelihoods with Bayesian generalised linear models using a modified version of the nonparametric variational inference algorithm presented in [4].
Large scale learning using stochastic gradient descent (ADADELTA).
[1] Yang, Z., Smola, A. J., Song, L., & Wilson, A. G. "A la Carte -- Learning Fast Kernels". Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, pp. 1098-1106, 2015.
[2] Le, Q., Sarlos, T., & Smola, A. "Fastfood-approximating kernel expansions in loglinear time." Proceedings of the international conference on machine learning. 2013.
[3] Rahimi, A., & Recht, B. "Random features for large-scale kernel machines." Advances in neural information processing systems. 2007.
[4] Gershman, S., Hoffman, M., & Blei, D. "Nonparametric variational inference". arXiv preprint arXiv:1206.4665 (2012).
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Supported Operating Systems: Platform Independent
- Data Formats: Numpy
- Tags: Stochastic Gradient Descent, Large Scale Learning, Nonparametric Bayes, Nonlinear Regression, Gaussian Processes, Generalized Linear Models, Spark, Fast Food, Random Features
- Archive: download here
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