Документ взят из кэша поисковой машины. Адрес оригинального документа : http://hea-www.harvard.edu/AstroStat/slog/groundtruth.info/AstroStat/slog/tag/svm/index.html
Дата изменения: Unknown
Дата индексирования: Sat Mar 1 09:36:23 2014
Кодировка:

Поисковые слова: molecular cloud
The AstroStat Slog » SVM

Posts tagged ‘SVM’

More on Space Weather

Thanks to a Korean solar physicist[1] 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’ »

  1. I must acknowledge him for his kindness and patience. He was my wikipedia to questions while I was studying the Sun.[]

space weather

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’ »

[ArXiv] 1st week, Feb. 2008

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’ »

[ArXiv] SVM and galaxy morphological classification, Sept. 10, 2007

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’ »