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- Description:
A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems.
Dependencies:
- python 2.5+
- cvxopt 1.0+ (for solving linear and quadratic programs)
- pythongrid (for using a cluster)
- cython 0.14.1 (for speeding up kernel computations)
Description
The projects currently prototyped:
- Ellipsoidal multiple instance learning
- Contextual bandits upper confidence bound algorithm (using GP)
- learning the output kernel using block coordinate descent
- difference of convex functions algorithms for sparse classfication
Ellipsoidal Multiple Instance Learning
- The code for eMIL is contained in mil.mi_classifier_emil
- emil_demo.py gives usage examples
- mil.mi_data provides containers for multiple instance learning data
Learning the output kernel using block coordinate descent
- The workhorse is kernelopt.py, which implements a framework for minimizing the invex function for learning the kernel on outputs.
- multiclass_demo.py gives usage examples.
- Changes to previous version:
- Included code for Gaussian Process Contextual Bandits
- Implemented Ellipsoidal Multiple Instance Learning
- difference of convex functions algorithms for sparse classfication
- BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Matlab, Hdf, Numpy, Json
- Tags: Kernel Learning
- Archive: download here
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