Sep 22nd, 2009| 12:03 pm | Posted by hlee
Thanks to a Korean solar physicist[] I was able to gather the following websites and some relevant information on Space Weather Forecast in action, not limited to literature nor toy data.
Continue reading ‘More on Space Weather’ »
Tags:
automatic,
CME,
computer vision,
data mining,
feature detection,
filament,
image processing,
machine learning,
manifold,
space weather,
statistical learning,
sunspot,
SVM Category:
Algorithms,
arXiv,
Cross-Cultural,
Data Processing,
Imaging,
Jargon |
Comment
May 21st, 2009| 05:55 pm | Posted by hlee
Among billion objects in our Galaxy, outside the Earth, our Sun drags most attention from astronomers. These astronomers go by solar physicists, who enjoy the most abundant data including 400 year long sunspot counts. Their joy is not only originated from the fascinating, active, and unpredictable characteristics of the Sun but also attributed to its influence on our daily lives. Related to the latter, sometimes studying the conditions on the Sun is called space weather forecast. Continue reading ‘space weather’ »
Tags:
classifier,
forecast,
logistic regression,
machine learning,
predictor,
response,
space weather,
Sun,
sunspot,
SVM,
test data,
training data,
weather Category:
arXiv,
Astro,
Cross-Cultural,
Data Processing,
Imaging,
Jargon,
Stars,
Stat |
Comment
Feb 10th, 2008| 11:56 am | Posted by hlee
Review papers on Bayesian hierarchical modeling and LAR (least angle regression) appeared in this week’s stat arXiv and in addition to interesting astro-ph papers.
A review paper on LASSO and LAR: [stat.ME:0801.0964] T. Hesterberg et.al.
Least Angle and L1 Regression: A Review
Model checking for Bayesian hierarchical modeling: [stat.ME:0802.0743] M. J. Bayarri, M. E. Castellanos
Bayesian Checking of the Second Levels of Hierarchical Models
Continue reading ‘[ArXiv] 1st week, Feb. 2008’ »
Tags:
CMB,
compressed sensing,
cosmic void,
experimental design,
hierarchical model,
ICA,
Kd-tree,
LAR,
LASSO,
Model Selection,
solar flare,
SVM Category:
arXiv |
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’ »