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W. Gasarch, David Mackay, Maulik Dave, Klaus Schneider
The Book Review Column 1 Coverage Untyped Systems Simple Types Recursive Types Higher-order Systems General Impression 3 Organization, and Contents of the Book
A. Gammerman, I. Nouretdinov, Brian Burford, A. Chervonenkis, V. Vovk, Zhiyuan Luo (2008)
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Fayin Li, H. Wechsler (2005)
Open set face recognition using transductionIEEE Transactions on Pattern Analysis and Machine Intelligence, 27
I. Nouretdinov, S. Costafreda, A. Gammerman, A. Chervonenkis, V. Vovk, V. Vapnik, C. Fu (2011)
Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depressionNeuroImage, 56
I. Nouretdinov, Dmitry Devetyarov, Brian Burford, S. Camuzeaux, A. Gentry-Maharaj, A. Tiss, Celia Smith, Zhiyuan Luo, A. Chervonenkis, R. Hallett, V. Vovk, M. Waterfield, R. Cramer, J. Timms, I. Jacobs, U. Menon, A. Gammerman (2012)
Multiprobabilistic Venn Predictors with Logistic Regression
V Vovk, A Gammerman, G Shafer (2005)
Algorithmic Learning in a Random World
V. Vovk, I. Nouretdinov, Alex Gammerman (2003)
Testing Exchangeability On-Line
R. Granetz, P. Smeulders (1988)
X-ray tomography on JETNuclear Fusion, 28
S. Ho, H. Wechsler (2010)
A Martingale Framework for Detecting Changes in Data Streams by Testing ExchangeabilityIEEE Transactions on Pattern Analysis and Machine Intelligence, 32
J. Vega, A. Murari, S. González, A. Pereira, I. Pastor (2013)
Spatial location of local perturbations in plasma emissivity derived from projections using conformal predictorsNuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment, 720
O. Lischtschenko, KE Bystrov, G. Temmerman, John Howard, R. Jaspers, Ralf König (2010)
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W. Martinez, Angel Martinez (2001)
Computational Statistics Handbook with MATLAB
A. Holland (1986)
Tomographic Analysis of the Evolution of Plasma Cross-Sections in Hbt.
Valentina Fedorova, A. Gammerman, I. Nouretdinov, V. Vovk (2012)
Plug-in martingales for testing exchangeability on-lineArXiv, abs/1204.3251
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Classical potential theory and its probabilistic counterpartMetrika, 33
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TABLE OF CONTENTS Preface
G. Shafer, V. Vovk (2007)
A tutorial on conformal predictionArXiv, abs/0706.3188
S. Ho, H. Wechsler (2007)
Detecting Changes in Unlabeled Data Streams Using Martingale
J. Vega, A. Murari, A. Pereira, S. González, I. Pastor (2010)
Accurate and reliable image classification by using conformal predictors in the TJ-II Thomson scattering.The Review of scientific instruments, 81 10
A. Lambrou, H. Papadopoulos, A. Gammerman (2011)
Reliable Confidence Measures for Medical Diagnosis With Evolutionary AlgorithmsIEEE Transactions on Information Technology in Biomedicine, 15
Vineeth Balasubramanian, R. Gouripeddi, S. Panchanathan, J. Vermillion, A. Bhaskaran, Robert Siegel (2009)
Support vector machine based conformal predictors for risk of complications following a coronary Drug Eluting Stent procedure2009 36th Annual Computers in Cardiology Conference (CinC)
A. Navarro, M. Ochando, A. Weller (1991)
Equilibrium-based iterative tomography technique for soft X-ray in stellaratorsIEEE Transactions on Plasma Science, 19
L Makili, J Vega, S Dormido-Canto (2013)
Incremental support vector machines for fast reliable image recognitionFusion Eng. Des., 88
H. Papadopoulos (2013)
Reliable probabilistic classification with neural networksNeurocomputing, 107
Lázaro Makili, J. Vega, S. Dormido, S. Dormido-Canto (2013)
INCREMENTAL SUPPORT VECTOR MACHINES FOR FAST RELIABLE INCREMENTAL SUPPORT VECTOR MACHINES FOR FAST RELIABLE IMAGE RECOGNITION
A. Navarro, V. Paré, J. Dunlap (1981)
Two‐dimensional spatial distribution of volume emission from line integral dataReview of Scientific Instruments, 52
A. Lambrou, H. Papadopoulos, I. Nouretdinov, A. Gammerman (2012)
Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets
Tomography is used to reconstruct the inside cross section of an inaccesible object. To this end, a high number of cross section projections are required. In nuclear fusion devices, the plasma follows a toroidal shape and tomography can be used to estimate the two-dimensional spatial distribution of the plasma emission through a cross section. One of the objectives of tomography in plasma physics is to determine the number of localized emission peaks within the cross-section. Due to space restrictions in fusion devices, only a very limited number of projections can be experimentally measured and this introduces limitations to detect the number of emission peaks in the cross-section. This article describes a set of simulations to show that a multi-class classification system can be a valid alternative to tomography in plasma physics, even with the data of a single projection. Each class of the classifier represents the number of perturbations that can appear at any time in the plasma emissivity. To ensure the accuracy and reliability of the predictions, probabilistic classifiers (Venn predictors and Bayesian classifiers) have been used, with more reliable results from the Venn predictors. The probability intervals of Venn predictors with a single projection and intense/low respectively perturbations are [0.85, 0.93] and [0.62, 0.71] (to be compared with a probability 0.2 of a class random assignation). However, when the perturbation peaks are not strong enough and are lost into the emissivity background, the determination of the number of emission peaks can produce unreliable results. In these cases, to detect the plasma transition from an unperturbed state to a perturbed one (regardless of the number of perturbations) a martingale framework has been used. The changes have been detected by testing exchangeability with two different martingales: randomized power martingale and simple mixture martingale. Neither false alarms nor missed alarms have been observed with the former one and the average delay in the change recognition is between 16 and 72 samples (depending on cases). This means between 16 and 72 ms if the sampling period is 1 ms (it should be noted that the discharge length in ITER will be 50 min).
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Jan 7, 2014
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