May 5th, 2008| 03:08 am | Posted by hlee
Since I learned Hubble’s tuning fork[] for the first time, I wanted to do classification (semi-supervised learning seems more suitable) galaxies based on their features (colors and spectra), instead of labor intensive human eye classification. Ironically, at that time I didn’t know there is a field of computer science called machine learning nor statistics which do such studies. Upon switching to statistics with a hope of understanding statistical packages implemented in IRAF and IDL, and learning better the contents of Numerical Recipes and Bevington’s book, the ignorance was not the enemy, but the accessibility of data was. Continue reading ‘[ArXiv] 5th week, Apr. 2008’ »
Tags:
ANN,
automation,
Classification,
correlation function,
denoising,
FFT,
gravitational wave,
lensing,
LISA,
machine learning,
missing data,
mock data,
morphology,
PCA,
power spectrum,
robust,
SDSS,
spectrum,
sunspots,
wavelet,
zoo Category:
arXiv,
Galaxies,
Imaging,
MCMC,
Physics,
Spectral |
Comment
Sep 12th, 2007| 04:31 pm | Posted by hlee
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.
Continue reading ‘[ArXiv] SVM and galaxy morphological classification, Sept. 10, 2007’ »