The AstroStat Slog

From arxiv/astro-ph:0709.1359,
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description by M. Huertas-Company et al.

Machine learning and statistical learning become more and more popular in astronomy. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are hardly missed when classifying on massive survey data is the objective. The authors provide a gentle tutorial on SVM for galactic morphological classification. Their source code GALSVM is linked for the interested readers.

One of the biggest challenges to apply SVM or other classification methods in astronomy is quantification of measures, or how to define parameters and variables physically meaningful and machine interpretable at the same time. The authors of arxiv/astro-ph:0709.1359 followed the idea of Abraham et. al. (1994), who introduced concentration. However, my impression so far tells me that standardized indices (like economic indicators) are hardly found for the classification purpose in astronomy. Astronomical Machine Learning consortium would accelerate understanding many populations in the Universe.