Support vector machine-based feature extractor for L/H transitions in JET

S. González, J. Vega, A. Murari, A. Pereira, J.M. Ramírez, S. Dormido-Canto

Research output: Contribution to journalArticle

8 Citations (Scopus)


Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained. © 2010 EURATOM.
Original languageEnglish
Article number10E123
Pages (from-to)-
JournalReview of Scientific Instruments
Issue number10
Publication statusPublished - Oct 2010
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Instrumentation

Cite this

González, S., Vega, J., Murari, A., Pereira, A., Ramírez, J. M., & Dormido-Canto, S. (2010). Support vector machine-based feature extractor for L/H transitions in JET. Review of Scientific Instruments, 81(10), -. [10E123].