Learning and evaluation in presence of Non-i.i.d. label noise

Journal of Machine Learning Research

Volumn 33, Issue , 2014, Pages 293-302

  Görnitz, Nico ^a   Porbadnigk, Anne K ^a   Binder, Alexander ^a   Sannelli, Claudia ^a   Braun, Mikio ^a   Müller, Klaus Robert ^a,b   Kloft, Marius ^c  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b Korea University   (South Korea)

^c MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BRAIN COMPUTER INTERFACE; DATA DESCRIPTION; LEARNING SYSTEMS;

BRAIN-COMPUTER INTERFACE APPLICATIONS; BRAIN-COMPUTER INTERFACING; CONTROLLED SIMULATION; DISTRIBUTED NOISE; MACHINE LEARNING METHODS; NOVEL METHODOLOGY; SUPPORT VECTOR DATA DESCRIPTION; SYSTEMATIC NOISE;

INTERFACES (COMPUTER);

EID: 84938246267     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (41)

1. V. Vapnik, Statistical learning theory. New York: John Wiley, 1998.
2. G. Dornhege, J. del R. Millán, T. Hinterberger, D. McFarland, and K.-R. Müller, eds., Toward Brain-Computer Interfacing. Cambridge, MA: MIT Press, 2007.
3. B. Blankertz, M. Tangermann, C. Vidaurre, S. Fazli, C. Sannelli, S. Haufe, C. Maeder, L. E. Ramsey, I. Sturm, G. Curio, and K.-R. Müller, "The Berlin Brain-Computer Interface: Non-medical uses of BCI technology," Front Neuroscience, vol. 4, p. 198, 2010. Open Access.
4. S. Lemm, B. Blankertz, T. Dickhaus, and K.-R. Müller, "Introduction to machine learning for brain imaging," Neuroimage, vol. 56, pp. 387-399, 2011.
5. A. K. Porbadnigk, M. S. Treder, B. Blankertz, J.-N. Antons, R. Schleicher, S. Möller, G. Curio, and K.-R. Müller, "Single-trial analysis of the neural correlates of speech quality perception," Journal of Neural Engineering, vol. 10(5), p. 056003, 2013.
6. A. K. Porbadnigk, N. Görnitz, M. Kloft, and K.-R. Müller, "Decoding brain states by supervising unsupervised learning," Journal of Computing Science and Engineering, vol. 7(2), pp. 112-121, 2013.
7. V. Vapnik, The nature of statistical learning theory. New York: Springer Verlag, 1995.
8. W. Polonik, "Measuring mass concentration and estimating density contour clusters - an excess mass approach," Annals of Statistics, vol. 23, pp. 855-881, 1995.
9. D. M. Tax and R. P. Duin, "Support vector data description," Machine Learning, vol. 54, pp. 45-66, 2004.
10. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, "An introduction to kernel-based learning algorithms," IEEE Neural Networks, vol. 12, pp. 181-201, May 2001.
11. B. Schölkopf and A. Smola, Learning with Kernels. Cambridge, MA: MIT Press, 2002.
12. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large Margin Methods for Structured and Interdependent Output Variables," Journal of Machine Learning Research, vol. 6, pp. 1453-1484, 2005.
13. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, pp. 1627-1645, Sept. 2010.
14. B. K. Sriperumbudur and G. R. G. Lanckriet, "On the convergence of the concave-convex procedure," in NIPS (Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, eds.), pp. 1759-1767, Curran Associates, Inc., 2009.
15. A. L. Yuille and A. Rangarajan, "The concave-convex procedure," Neural Computation, vol. 15, no. 4, pp. 915-936, 2003.
16. E. Yen, N. Peng, P.-W. Wang, and S.-D. Lin, "On convergence rate of concave-convex procedure," Proceedings of the NIPS 2012 Optimization Workshop, 2012.
17. G. Rätsch, S. Mika, B. Schölkopf, and K.-R. Müller, "Constructing boosting algorithms from SVMs: An application to one-class classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1184-1199, Sept. 2002.
18. N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. S. Kandola, "On kernel target alignment," in Advances in Neural Information Proccessing Systems (NIPS), vol. 14, pp. 367-737, 2001.
19. C. Cortes, M. Mohri, and A. Rostamizadeh, "Algorithms for learning kernels based on centered alignment," J. Mach. Learn. Res., vol. 13, pp. 795-828, Mar. 2012.
20. C. Cortes and M. Mohri, "Confidence intervals for the area under the roc curve," in NIPS, 2004.
21. M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.-R. Müller, and A. Zien, "Efficient and accurate lp-norm multiple kernel learning," in Advances in Neural Information Processing Systems 22 (Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, eds.), pp. 997-1005, MIT Press, 2009.
22. P. Bartlett and S. Mendelson, "Rademacher and gaussian complexities: Risk bounds and structural results," Journal of Machine Learning Research, vol. 3, pp. 463-482, Nov. 2002.
23. M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning. The MIT Press, 2012.
24. M. Mohri and A. Rostamizadeh, "Rademacher complexity bounds for non-i.i.d. processes," in NIPS (D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, eds.), pp. 1097-1104, Curran Associates, Inc., 2008.
25. B. Blankertz, S. Lemm, M. S. Treder, S. Haufe, and K.-R. Müller, "Single-trial analysis and classification of ERP components - a tutorial," Neuroimage, vol. 56, pp. 814-825, 2011.
26. M. L. Braun, J. Buhmann, and K.-R. Müller, "On relevant dimensions in kernel feature spaces," Journal of Machine Learning Research, vol. 9, pp. 1875-1908, Aug 2008.
27. G. Montavon, M. L. Braun, T. Krueger, and K.-R. Müller, "Analyzing local structure in kernel-based learning: Explanation, complexity and reliability assessment," IEEE Signal Processing Magazine, Special Issue on Kernel-Based Learning for Signal Processing, vol. 30(4), pp. 62-74, 2013.
28. B. Schölkopf, A. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput, vol. 10, no. 5, pp. 1299-1319, 1998.
29. X. Zhu and Z. Ghahramani, "Learning from labeled and unlabeled data with label propagation," tech. rep., Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002.
30. X. Zhu, Z. Ghahramani, and J. Lafferty, "Semi-supervised learning using gaussian fields and harmonic functions," in International Conference on Machine Learning (ICML), vol. 20, p. 912, 2003.
31. N. Görnitz, M. Kloft, and U. Brefeld, "Active and semi-supervised data domain description," in Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (W. L. Buntine, M. Grobelnik, D. Mladenic, and J. Shawe-Taylor, eds.), pp. 407-422, 2009.
32. N. Görnitz, M. Kloft, K. Rieck, and U. Brefeld, "Toward Supervised Anomaly Detection.," Journal of Artificial Intelligence Research (JAIR), vol. 46, pp. 235-262, 2013.
33. M. Falkenstein, J. Hoormann, S. Christ, and J. Hohnsbein, "ERP components on reaction errors and their functional significance: a tutorial," Biol Psychol, vol. 51, no. 2-3, pp. 87-107, 2000.
34. J. Hohnsbein, M. Falkenstein, and J. Hoormann, "Error processing in visual and auditory choice reaction tasks," Journal of Psychophysiology, vol. 3, p. 320, 1998.
35. M. Falkenstein, J. Hohnsbein, J. Hoormann, and L. Blanke, "Effects of errors in choice reaction tasks on the ERP under focused and divided attention," in Psychophysiological Brain Research (C. Brunia, A. Gaillard, and A. Kok, eds.), pp. 192-195, Tilburg University Press, Tilburg, 1990.
36. W. Gehring, M. Coles, D. Meyer, and E. Donchin, "The error-related negativity: an event-related brain potential accompanying errors," Psychophysiology, vol. 27, p. S34, 1990.
37. W. Gehring, B. Goss, M. Coles, D. Meyer, and E. Donchin, "A neural system for error detection and compensation," Psychological Science, vol. 4, pp. 385-390, 1993.
38. R. Brickenkamp and E. Zillmer, D2 Test of Attention. Göttingen, Germany: Hogrefe & Huber, 1998.
39. F. Bießmann, F. C. Meinecke, A. Gretton, A. Rauch, G. Rainer, N. Logothetis, and K.-R. Müller, "Temporal kernel canonical correlation analysis and its application in multimodal neuronal data analysis," Machine Learning, vol. 79, no. 1-2, pp. 5-27, 2009.
40. F. Bießmann, S. M. Plis, F. C. Meinecke, T. Eichele, and K.-R. Müller, "Analysis of multimodal neuroimaging data," Biomedical Engineering, IEEE Reviews in, vol. 4, pp. 26-58, 2011.
41. M. Talagrand, "Concentration of measure and isoperimetric inequalities in product spaces," Publications Mathmatiques de L'IHS, vol. 81, pp. 73-205, 1995.