Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression

Neural Networks

Volumn 41, Issue , 2013, Pages 59-69

  Gijsberts, Arjan ^a   Metta, Giorgio ^a  

^a ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

Author keywords

Function approximation; Incremental learning; Online learning; Real time; Robotics

Indexed keywords

FUNCTION APPROXIMATION; GAUSSIAN PROCESS REGRESSION; HYPER-PARAMETER OPTIMIZATIONS; INCREMENTAL LEARNING; LOCALLY WEIGHTED PROJECTION REGRESSIONS; ONLINE LEARNING; REAL-TIME; SPARSE SPECTRUM GAUSSIAN PROCESS;

ALGORITHMS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); ROBOTICS;

REGRESSION ANALYSIS;

ARTICLE; AUTOMATION; BAYES THEOREM; DATA ANALYSIS; INCREMENTAL SPARSE SEPCTRUM GAUSSIAN PROCESS REGRESSION; INTERMETHOD COMPARISON; KERNEL METHOD; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; ONLINE ANALYSIS; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION; ROBOTICS; VALIDATION PROCESS; VISUOMOTOR COORDINATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SYSTEMS; HUMANS; MODELS, THEORETICAL; NORMAL DISTRIBUTION; REGRESSION ANALYSIS; ROBOTICS;

EID: 84875887187     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.08.011     Document Type: Article

References (48)

1. Björck Å Numerical methods for least squares problems 1996, SIAM, Philadelphia.
2. Bochner S. Monotone Funktionen, Stieltjessche Integrale und Harmonische Analyse. Mathematische Annalen 1933, 108:378-410.
3. Bordes A., Ertekin S., Weston J., Bottou L. Fast kernel classifiers with online and active learning. Journal of Machine Learning Research 2005, 6:1579-1619.
4. Cauwenberghs G., Poggio T. Incremental and decremental support vector machine learning. Advances in neural information processing systems 13 2001, 409-415. MIT Press.
5. Cavallanti G., Cesa-Bianchi N., Gentile C. Tracking the best hyperplane with a simple budget perceptron. Machine Learning 2007, 69:143-167.
6. Cesa-Bianchi N., Gentile C. Improved risk tail bounds for on-line algorithms. IEEE Transactions on Information Theory 2008, 54:386-390.
7. Cesa-Bianchi N., Lugosi G. Prediction, learning, and games 2006, Cambridge University Press, New York, NY, USA.
8. Crammer K., Kandola J.S., Singer Y. Online classification on a budget. Advances in neural information processing systems, vol. 16 2004, MIT Press, Cambridge, MA, USA.
9. Csató L., Opper M. Sparse on-line Gaussian processes. Neural Computation 2002, 14:641-668.
10. Dekel O., Shalev-Shwartz S., Singer Y. The Forgetron: a kernel-based perceptron on a budget. SIAM Journal on Computing 2008, 37:1342-1372.
11. Dongarra J.J., Bunch J.R., Moler C.B., Stewart G.W. LINPACK users' guide 1979, Society for Industrial and Applied Mathematics, Philadelphia, PA.
12. Droniou, A., Ivaldi, S., Stalph, P., Butz, M., & Sigaud, O. (2012). Learning velocity kinematics: experimental comparison of on-line regression algorithms. In Proceedings robotica (pp. 15-20).
13. Engel Y., Mannor S., Meir R. Sparse online greedy support vector regression. Proceedings of the 13th European conference on machine learning 2002, 84-96. Springer-Verlag, London, UK.
14. Engel Y., Mannor S., Meir R. The kernel recursive least squares algorithm. IEEE Transactions on Signal Processing 2004, 52:2275-2285.
15. Fumagalli M., Gijsberts A., Ivaldi S., Jamone L., Metta G., Natale L., et al. Learning to exploit proximal force sensing: a comparison approach. From motor learning to interaction learning in robots 2010, 149-167. Springer.
16. Gijsberts, A., & Metta, G. (2011). Incremental learning of robot dynamics using random features. In IEEE International conference on robotics and automation(pp. 951-956).
17. Goldsmith S.F., Aiken A.S., Wilkerson D.S. Measuring empirical computational complexity. Proceedings of the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering 2007, 395-404. ACM, New York, NY, USA.
18. Golub G.H., Loan C.F.V. Matrix computations 1996, Johns Hopkins University Press, Baltimore, MD, USA. 3rd ed.
19. Ivaldi, S., Fumagalli, M., Randazzo, M., Nori, F., Metta, G., & Sandini, G. (2011). Computing robot internal/external wrenches by means of inertial, tactile and f/t sensors: theory and implementation on the iCub. In Proceedings of the 11th IEEE-RAS international conference on humanoid robots.
20. Jamone, L., Nori, F., Metta, G., & Sandini, G. (2006). James: a humanoid robot acting over an unstructured world. In International conference on humanoid robots(pp. 143-150).
21. Kersting K., Plagemann C., Pfaff P., Burgard W. Most likely heteroscedastic Gaussian process regression. Proceedings of the 24th international conference on machine learning 2007, 393-400. ACM, New York, NY, USA.
22. Kivinen J., Smola A.J., Williamson R.C. Online learning with kernels. IEEE Transactions on Signal Processing 2004, 52:2165-2176.
23. Klanke S., Vijayakumar S., Schaal S. A library for locally weighted projection regression. Journal of Machine Learning Research 2008, 9:623-626.
24. Lázaro-Gredilla, M., Quiñonero-Candela, J., & Figueiras-Vidal, A.R. (2007). Sparse spectral sampling Gaussian processes. Technical Report MSR-TR-2007-152. Microsoft Research.
25. Lázaro-Gredilla M., Quiñonero-Candela J., Rasmussen C.E., Figueiras-Vidal A.R. Sparse spectrum Gaussian process regression. Journal of Machine Learning Research 2010, 11:1865-1881.
26. Ma J., Theiler J., Perkins S. Accurate on-line support vector regression. Neural Computation 2003, 15:2683-2703.
27. MacKay D.J.C. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems 1995, 6:469-505.
28. Martin M. On-line support vector machine regression. Proceedings of the 13th European conference on machine learning 2002, 282-294. Springer-Verlag, London, UK.
29. Metta G., Natale L., Nori F., Sandini G., Vernon D., Fadiga L., et al. The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Networks 2010, 23:1125-1134.
30. Natale, L., Nori, F., Sandini, G., & Metta, G. (2007). Learning precise 3D reaching in a humanoid robot. In IEEE International conference of development and learning (pp. 324-329).
31. Nguyen-Tuong D., Seeger M.W., Peters J. Model learning with local Gaussian process regression. Advanced Robotics 2009, 23:2015-2034.
32. Orabona F., Keshet J., Caputo B. Bounded kernel-based online learning. Journal of Machine Learning Research 2009, 10:2643-2666.
33. Rahimi A., Recht B. Random features for large-scale kernel machines. Advances in neural information processing systems 20 2008, 1177-1184. MIT Press, Cambridge, MA.
34. Rahimi, A., & Recht, B. (2008b). Uniform approximation of functions with random bases. In Allerton conference on communication control and computing, Allerton08 (pp. 555-561).
35. Rasmussen C.E., Williams C.K.I. Gaussian processes for machine learning. Adaptive computation and machine learning 2005, The MIT Press.
36. Sayed A.H. Adaptive filters 2008, Wiley-IEEE Press.
37. Schaal S., Atkeson C.G. Constructive incremental learning from only local information. Neural Computation 1998, 10:2047-2084.
38. Schneider, M., & Ertel, W. (2010). Robot learning by demonstration with local Gaussian process regression. In IEEE/RSJ international conference on intelligent robots and systems (pp. 255-260).
39. Shawe-Taylor J., Cristianini N. Kernel methods for pattern analysis 2004, Cambridge University Press.
40. Sigaud O., Salaün C., Padois V. On-line regression algorithms for learning mechanical models of robots: a survey. Robotics and Autonomous Systems 2011, 59:1115-1129.
41. Spong M.W., Hutchinson S., Vidyasagar M. Robot modeling and control 2006, Wiley, New York, USA.
42. Strehl A., Littman M. Online linear regression and its application to model-based reinforcement learning. Advances in neural information processing systems 20 2008, 1417-1424. MIT Press, Cambridge, MA.
43. Vijayakumar S., D'souza A., Schaal S. Incremental online learning in high dimensions. Neural Computation 2005, 17:2602-2634.
44. Vijayakumar, S., & Schaal, S. (2000). Locally weighted projection regression: an O(n) algorithm for incremental real time learning in high dimensional spaces. In Proceedings of the 17th international conference on machine learning, ICML '00 (pp. 288-293).
45. Vijayakumar S., Wu S. Sequential support vector classifiers and regression. Proceedings of the third ICSC symposia on intelligent industrial automation and soft computing 1999, 610-619. ICSC Academic Press.
46. Vovk V., Gammerman A., Shafer G. Algorithmic learning in a random world 2005, Springer-Verlag New York, Inc., Secaucus, NJ, USA.
47. Weston J., Bordes A., Bottou L. Online (and offline) on an even tighter budget. Proceedings of the tenth international workshop on artificial intelligence and statistics 2005, 413-420. Society for Artificial Intelligence and Statistics.
48. Zhdanov F., Kalnishkan Y. An identity for kernel ridge regression. Proceedings of the 21st international conference on algorithmic learning theory 2010, 405-419. Springer-Verlag, Berlin, Heidelberg.