Multiclass Active Learning Algorithms with Application in Astronomy.
| Contributors: | Alasdair Tran, Cheng Soon Ong, Jakub Nabaglo, David Wu, Wei Yen Lee |
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| License: | This package is distributed under a a 3-clause ("Simplified" or "New") BSD license. |
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| Publications: | Combining Active Learning Suggestions by Alasdair Tran, Cheng Soon Ong, and Christian Wolf Active Learning with Gaussian Processes by Jakub Nabaglo Photometric Classification with Thompson Sampling by Alasdair Tran Cutting-Plane Methods with Active Learning by David Wu |
This repository contains a collection of projects related to active learning methods with application in astronomy. Click on one of the links below to go to the directory of a particular project.
- Combining Active Learning Suggestions by Alasdair Tran, Cheng Soon Ong, and Christian Wolf
- Active Learning with Gaussian Processes for Photometric Redshift Prediction
- Cutting-plane Methods with Applications in Convex Optimization and Active Learning
- Photometric Classification with Thompson Sampling
mclearn is a Python package that implement selected multiclass active learning algorithms, with a focus in astronomical data.
The dependencies are Python 3.4, numpy, pandas, matplotlib, seaborn, ephem, scipy, ipython, and scikit-learn. It's best to first install the Anaconda distribution for Python 3, then install mclearn using pip:
pip install mclearn