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Support vector machines for disruption prediction and novelty detection at JET

✍ Scribed by B. Cannas; R.S. Delogu; A. Fanni; P. Sonato; M.K. Zedda


Book ID
108137043
Publisher
Elsevier Science
Year
2007
Tongue
English
Weight
245 KB
Volume
82
Category
Article
ISSN
0920-3796

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## Abstract The support vector machines (SVMs) method is proposed because it can reflect the sequence‐coupling effect for a tetrapeptide in not only a β‐turn or non‐β‐turn, but also in different types of β‐turn. The results of the model for 6022 tetrapeptides indicate that the rates of self‐consist