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

Review of Scientific Instruments

Volumn 81, Issue 10, 2010, Pages

  Gonzalez S ^a,b   Vega, J ^a,b   Murari, A ^a,c   Pereira, A ^a,b   Ramirez J M ^a,d   Dormido Canto, S ^a,d  

^a CULHAM SCIENCE CENTRE   (United Kingdom)

^b LABORATORIO NACIONAL DE FUSIÓN   (Spain)

^c Associazione EURATOM ENEA per la Fusione ^*  (Italy)

^d UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CONFINEMENT TRANSITIONS; FEATURE EXTRACTOR; JET DISCHARGES; LOW-TO-HIGH; MACHINE-LEARNING; OPTIMAL NUMBER; TOKAMAK PLASMAS; WAVE FORMS;

ARTIFICIAL INTELLIGENCE; FUSION REACTORS; GEARS; MAGNETOPLASMA;

SUPPORT VECTOR MACHINES;

EID: 78149426765     PISSN: 00346748     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.3502327     Document Type: Conference Paper

References (6)

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2. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees (Wadsworth, Inc., Belmont, CA / Chapman and Hall, New York, 1993).
3. J. Lattin, J. D. Carroll, and P. E. Green, Analyzing Multivariate Data (Thomson Brooks/Cole, Duxbury, 2003).
4. V. N. Vapnik, Statistical Learning Theory (Wiley, New York, 1998).
5. V. Cherkassky and F. Mulier, Learning from Data: Concepts, Theory and Methods, 2nd ed. (Wiley, New York, 2007).
6. J. Ramírez, S. Dormido-Canto, J. Vega, and JET-EFDA Contributors, Fusion Eng. Des. FEDEEE 0920-3796 80, 425 (2010). 10.1016/j.fusengdes.2009.10. 003