I2VM: Incremental import vector machines

Image and Vision Computing

Volumn 30, Issue 4-5, 2012, Pages 263-278

  Roscher, Ribana ^a   Förstner, Wolfgang ^a   Waske, Björn ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Concept drifts; Import vector machines; Incremental learning

Indexed keywords

COMPUTER APPLICATIONS; ELECTRICAL ENGINEERING;

COMPLEX DATA DISTRIBUTIONS; CONCEPT-DRIFTS; DATA DISTRIBUTION; DATA SAMPLE; DISCRIMINATIVE APPROACH; INCREMENTAL LEARNING; INCREMENTAL METHOD; INCREMENTAL UPDATES; MODEL PARAMETERS; PROBABILISTIC OUTPUT; TRAINING AND TESTING; VECTOR MACHINES;

VECTORS;

EID: 84861743393     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2012.04.004     Document Type: Article

References (88)

1. Y. Bengio, J. Louradour, R. Collobert, and J. Weston Curriculum learning Proceedings of the 26th Annual International Conference on Machine Learning 2009 ACM 41 48
2. C. Bishop Pattern Recognition and Machine Learning vol. 4 2006 Springer New York
3. C. Bodart, H. Eva, R. Beuchle, R. Rasi, D. Simonetti, H. Stibig, A. Brink, E. Lindquist, and F. Achard Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics ISPRS J. Photogramm. Remote Sens. 66 2011 555 563
4. A. Bordes, L. Bottou, P. Gallinari, and J. Weston Solving MultiClass support vector machines with LaRank Proc. International Conference on Machine Learning 2007
5. A.C. Braun, U. Weidner, and S. Hinz Support vector machines, import vector machines and relevance vector machines for hyperspectral classification - a comparison Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing June 2011 1 4
6. L. Breiman Bagging Predictors Machine Learning 24 2 1996 123 140 (Pubitemid 126724382)
7. L. Breiman Random forests Mach. Learn. 45 1 2001 5 32 (Pubitemid 32933532)
8. L. Breiman, J. Friedman, R. Olshen, C. Stone, L. Breiman, W. Hoeffding, R. Serfling, J. Friedman, O. Hall, and P. Buhlmann Classification and regression trees Mach. Learn. 19 1984 293 325
9. R. Caruana, and A. Niculescu-Mizil An empirical comparison of supervised learning algorithms Proc. International Conference on Machine Learning 2006 ACM 161 168
10. G. Cauwenberghs, and T. Poggio Incremental and decremental support vector machine learning Proc. Advances in Neural Information Processing Systems 2001 409 415
11. G. Cawley, and N. Talbot Efficient model selection for kernel logistic regression Pattern Recognit. 2 2004 439 442
12. G. Cawley, N. Talbot, and M. Girolami Sparse multinomial logistic regression via Bayesian L1 regularisation Proc. Advances in Neural Information Processing Systems 2007
13. C. Chang, and C. Lin LIBSVM: A Library for Support Vector Machines 2001
14. N. Chawla, and D. Cieslak Evaluating probability estimates from decision trees American Association for Artificial Intelligence 2006
15. T. Chin, and D. Suter Incremental kernel principal component analysis IEEE Trans. Image Process. 16 6 2007 1662 1674 (Pubitemid 46804335)
16. J. Cihlar Land cover mapping of large areas from satellites: status and research priorities Int. J. Remote Sens. 21 6-7 2000 1093 1114 (Pubitemid 30242127)
17. J. Cohen A coefficient of agreement for nominal scales Educ. Psychol. Meas. 20 1 1960 37 46
18. A. Cornuéjols Getting order independence in incremental learning Proc. European Conference on Machien Learning 1993 Springer 196 212
19. J. Cramer Logit Models from Economics and Other Fields 2003 Cambridge University Press
20. N. Dalal, and B. Triggs Histograms of oriented gradients for human detection Proc. IEEE Conference on Computer Vision and Pattern Recognition 2005 886 893 (Pubitemid 43897286)
21. T. Deselaers, T. Gass, G. Heigold, and H. Ney Latent log-linear models for handwritten digit classification IEEE Transactions on Pattern Analysis and Machine Intelligence 2011
22. L. Fei-Fei, R. Fergus, and P. Perona Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories Comput. Vis. Image Underst. 106 2007 59 70 (Pubitemid 46528761)
23. V. Franc, A. Zien, and B. Schlkopf Support vector machines as probabilistic models Proc. International Conference on Machine Learning 2011 665 672
24. S. Franklin, and M. Wulder Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas Progr. Phys. Geogr. 26 2 2002 139 172
25. Y. Freund, and R. Schapire Experiments With a New Boosting Algorithm International Conference on Machine Learning 1996 Morgan Kaufmann Publishers, Inc. 148 156
26. J. Friedman, T. Hastie, and R. Tibshirani Additive logistic regression: a statistical view of boosting Ann. Stat. 28 2000 337 407 (Pubitemid 33227445)
27. M. Fritz, B. Leibe, B. Caputo, and B. Schiele Integrating representative and discriminative models for object category detection Proc. IEEE International Conference on Computer Vision 2005
28. G. Fung, and O. Mangasarian Incremental support vector machine classification Proc. International Conference on Knowledge Discovery and Data Mining 2002 247 260
29. F. Giacco, C. Thiel, L. Pugliese, S. Scarpetta, and M. Marinaro Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs IEEE Trans. Geosci. Remote Sens. 99 2010 1 11
30. M. Genton Classes of kernels for machine learning: a statistics perspective The Journal of Machine Learning Research 2 2002 299 312
31. F. Glover Tabu Search-Part I ORSA Journal on Computing 1 3 1989 190 206
32. H. Grabner, and H. Bischof On-line boosting and vision Proc. IEEE Conference on Computer Vision and Pattern Recognition 2006 260 267 (Pubitemid 44931339)
33. H. Grabner, C. Leistner, and H. Bischof Semi-supervised on-line boosting for robust tracking Proc. IEEE European Conference on Computer Vision 2008 Springer 234 247
34. Y. Grandvalet, J. Mariéthoz, and S. Bengio Interpretation of SVMS with an application to unbalanced classification Proc. Advances in Neural Information Processing Systems 2005
35. S. Gu, Y. Zheng, C. Tomasi, S. Gu, Y. Zheng, and C. Tomasi Efficient visual object tracking with online nearest neighbor classifier Proc. Asian Conference on Computer Vision 2010
36. T. Hastie, R. Tibshirani, and J. Friedman The elements of statistical learning Springer Series in Statistics vol. 1 2009
37. N. Higham Accuracy and Stability of Numerical Algorithms 2002 Society for Industrial Mathematics
38. T. Ho The Random Subspace Method for Constructing Decision Forests IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 1998 832 844 (Pubitemid 128741345)
39. J. Iglesias, E. Konukoglu, A. Montillo, Z. Tu, and A. Criminisi Combining generative and discriminative models for semantic segmentation of ct scans via active learning Proc. Information Processing in Medical Imaging 2011 Springer 25 36
40. M. Karasuyama, and I. Takeuchi Multiple incremental decremental learning of support vector machines IEEE Trans. Neural Netw. 21 7 2010 1048 1059
41. P. Karsmakers, K. Pelckmans, and J. Suykens Multi-class kernel logistic regression: a fixed-size implementation Proc. of the International Joint Conference on Neural Networks 2007 1756 1761
42. S. Keerthi, K. Duan, S. Shevade, and A. Poo A fast dual algorithm for kernel logistic regression Mach. Learn. 61 1 2005 151 165 (Pubitemid 43049366)
43. T. Kim, S. Wong, B. Stenger, J. Kittler, and R. Cipolla Incremental linear discriminant analysis using sufficient spanning set approximations Proc. IEEE Conference on Computer Vision and Pattern Recognition 2007 IEEE 1 8
44. J. Kivinen, A. Smola, and R. Williamson Online learning with kernels IEEE Trans. Signal Process. 52 8 2004 2165 2176
45. R. Klinkenberg, and T. Joachims Detecting concept drift with support vector machines Proc. International Conference on Machine Learning vol. 11 2000 Morgan Kaufmann San Francisco, CA, USA
46. J. Knorn, A. Rabe, V. Radeloff, T. Kuemmerle, J. Kozak, and P. Hostert Land cover mapping of large areas using chain classification of neighboring Landsat satellite images Remote Sens. Environ. 113 5 2009 957 964
47. B. Krishnapuram, L. Carin, M. Figueiredo, and A. Hartemink Sparse multinomial logistic regression: fast algorithms and generalization bounds IEEE Trans. Pattern Anal. Mach. Intell. 2005 957 968 (Pubitemid 40889845)
48. M. Kristan, and A. Leonardis Online discriminative kernel density estimation Proc. IEEE Conference on Computer Vision and Pattern Recognition 2010 IEEE 581 584
49. S. Kumar, and M. Hebert Discriminative random fields Int. J. Comput. Vis. 68 2 2006 179 201 (Pubitemid 43724245)
50. P. Langley Learning in humans and machines Learning towards an interdisciplinary learning science Ch. Order Effects in Incremental vol. 136 1995 Pergamon 141
51. J. Lasserre, C. Bishop, and T. Minka Principled hybrids of generative and discriminative models Proc. IEEE Conference on Computer Vision and Pattern Recognition vol. 1 2006 IEEE 87 94 (Pubitemid 44931316)
52. J. Li, J. Bioucas-Dias, and A. Plaza Hyperspectral image segmentation using a new Bayesian approach with active learning IEEE Trans. Geosci. Remote Sens. 49 99 2011 3947 3960
53. A. McCallum, C. Pal, G. Druck, and X. Wang Multi-conditional learning: generative/discriminative training for clustering and classification Proc. of the National Conference on Artificial Intelligence vol. 21 1999 AAAI Press; MIT Press Menlo Park, CA; Cambridge, MA; London 433
54. K. McKusick, and P. Langley Constraints on tree structure in concept formation Proc. International Joint Conference on Artificial Intelligence vol. 810 1991 816 Citeseer
55. A. Niculescu-Mizil, and R. Caruana Predicting good probabilities with supervised learning Proc. International Conference on Machine Learning 2005 ACM 625 632 (Pubitemid 43183386)
56. S. Pang, S. Ozawa, and N. Kasabov Incremental linear discriminant analysis for classification of data streams IEEE Trans. Syst. Man Cybern. 35 2005 905 914 (Pubitemid 41444576)
57. A. Pekkarinen, L. Reithmaier, and P. Strobl Pan-European forest/non-forest mapping with Landsat ETM + and CORINE Land Cover 2000 data ISPRS J. Photogramm. Remote Sens. 64 2 2009 171 183
58. J. Platt, N. Cristianini, and J. Shawe-Taylor Large margin DAGs for multiclass classification Proc. Advances in Neural Information Processing Systems vol. 12 2000 547 553 Citeseer
59. F. Provost, and P. Domingos Tree induction for probability-based ranking Mach. Learn. 52 3 2003 199 215
60. R. Raina, Y. Shen, A. Ng, and A. McCallum Classification with hybrid generative/discriminative models Proc. of the International Joint Conference on Neural Networks vol. 16 2003
61. J. Richards Analysis of remotely sensed data: the formative decades and the future IEEE Trans. Geosci. Remote Sens. 43 3 2005 422 432 (Pubitemid 40320265)
62. R. Roscher, B. Waske, and W. Förstner Incremental import vector machines for large area land cover classification Proc. IEEE/ISPRS Workshop on Computer Vision for Remote Sensing of the Environment 2011
63. R. Roscher, B. Waske, and W. Förstner Incremental import vector machines for classifying hyperspectral data IEEE Trans. Geosci. Remote Sens. 9 2012 1 11
64. V. Roth Probabilistic discriminative kernel classifiers for multi-class problems Pattern Recognit. 2191 2001 246 253 (Pubitemid 33347119)
65. S. Rüping Incremental learning with support vector machines Proc. IEEE International Conference on Data Mining 2001 IEEE 641 642
66. S. Rüping A simple method for estimating conditional probabilities in SVMs LWA 2004-Lernen-Wissensentdeckung-Adaptivität 2004 Humboldt-Universität Citeseer
67. A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof On-line random forests Proc. On-line Learning for Computer Vision Workshop 2009 1393 1400
68. B. Schölkopf, and A. Smola Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2002 MIT Press
69. M. Scholz, and R. Klinkenberg Boosting classifiers for drifting concepts Intell. Data Anal. 11 1 2007 3 28
70. A.K. Seewald Digits - A Dataset for Handwritten Digit Recognition Tech. rep. 2005 Austrian Research Institute for Artificial Intelligence TR-2005-27
71. D. Skocaj, M. Uray, A. Leonardis, and H. Bischof Why to combine reconstructive and discriminative information for incremental subspace learning Proc. Computer Vision Winter Workshop 2006
72. P. Sollich Bayesian methods for support vector machines: evidence and predictive class probabilities Mach. Learn. 46 1 2002 21 52 (Pubitemid 34129962)
73. L. Talaveral, and J. Roure A buffering strategy to avoid ordering effects in clustering Proc. European Conference on Machine Learning 1998 Springer 316 321 (Pubitemid 128067197)
74. F. Tang, S. Brennan, Q. Zhao, and H. Tao Co-tracking using semi-supervised support vector machines Proc. IEEE International Conference on Computer Vision 2007 1 8 URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper. htm?ar number = 4408954
75. M. Tipping Sparse Bayesian learning and the relevance vector machine J. Mach. Learn. Res. 1 2001 211 244 (Pubitemid 33687203)
76. Z. Tu Learning generative models via discriminative approaches Proc. IEEE Conference on Computer Vision and Pattern Recognition 2007 IEEE 1 8
77. D. Tuia, F. Ratle, F. Pacifici, M. Kanevski, and W. Emery Active learning methods for remote sensing image classification IEEE Trans. Geosci. Remote Sens. 47 7 2009 2218 2232
78. M. Uray, D. Skocaj, P.M. Roth, H. Bischof, and A. Leonardis Incremental LDA learning by combining reconstructive and discriminative approaches Proc. British Machine Vision Conference 2007
79. V. Vapnik The Nature of Statistical Learning Theory 2000 Springer Verlag
80. P. Viola, and M. Jones Robust real-time face detection Int. J. Comput. Vis. 57 2 2004 137 154
81. W. Walker, C. Stickler, J. Kellndorfer, K. Kirsch, and D. Nepstad Large-Area classification and mapping of forest and land cover in the Brazilian Amazon: a comparative analysis of ALOS/PALSAR and Landsat data sources IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 3 4 2010 594 604
82. S. Wenzel, and L. Hotz The role of sequences for incremental learning J. Filipe, A.L.N. Fred, B. Sharp, Proc. International Conference on Agents and Artificial Intelligence vol. 1 January 22-24 2010 INSTICC Press Valencia, Spain 434 439
83. J. Xue, and D. Titterington On the generative-discriminative tradeoff approach: interpretation, asymptotic efficiency and classification performance Comput. Stat. Data Anal. 54 2 2010 438 451
84. K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. Okuno An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model IEEE Trans. Audio Speech Lang. Process. 16 2008 435 447
85. B. Zadrozny, and C. Elkan Learning and making decisions when costs and probabilities are both unknown Proc. International Conference on Knowledge Discovery and Data Mining 2001 ACM 204 213
86. Z. Zhang, G. Dai, and M. Jordan Bayesian generalized kernel mixed models J. Mach. Learn. Res. 12 2011 111 139
87. J. Zheng, H. Yu, F. Shen, and J. Zhao An online incremental learning support vector machine for large-scale data Proc. International Conference on Artificial Neural Networks 2010 Springer 76 81
88. J. Zhu, and T. Hastie Kernel logistic regression and the import vector machine Comput. Graph. Stat. 14 2005 185 205 (Pubitemid 40447832)