How far do we get using machine learning black-boxes?

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 26, Issue 2, 2012, Pages

  Rocha, Anderson ^a   Papa, João Paulo ^b   Meira, Luis A A ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

binary to multi class classifiers; K nearest neighbors; Machine learning black boxes; optimum path forest; support vector machines; visual words

Indexed keywords

BINARY CLASSIFIERS; FEATURE SPACE; GOOD RESEARCH; K-NEAREST NEIGHBORS; MULTI-CLASS CLASSIFIER; MULTI-CLASS PROBLEMS; OPTIMUM-PATH FORESTS; VISUAL WORD;

MACHINERY; RESEARCH; SUPPORT VECTOR MACHINES;

LEARNING SYSTEMS;

EID: 84865782512     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001412610010     Document Type: Article

References (34)

1. E. Allwein, R. Shapire and Y. Singer, Reducing multi-class to binary: A unifying approach for margin classifiers, J. Mach. Learn. Res. 1(1) (2000) 113-141.
2. R. Anand, K. Mehrotra, C. Mohan and S. Ranka, Effcient classification for multi-class problems using modular neural networks, IEEE Trans. Neural Netw. 6(1) (1995) 117-124.
3. F. A. Andaló, P. A. V. Miranda, R. Torres and A. Falcão, Shape feature extraction and description based on tensor scale, Pattern Recogn. 43(1) (2010) 26-36.
4. T. R. Andel and A. Yasinsac, On the credibility of Manet simulations, Computer 39(7) (2006) 48-54.
5. N. Arica and F. T. Y. Vural, BAS: A perceptual shape descriptor based on the beam angle statistics, Pattern Recogn. Lett. 24(9-10) (2003) 1627-1639.
6. R. Baeza-Yates, Clustering and Information Retrieval, 1st edn. (Kluwer Academic Publishers, 2003).
7. H. Bay, T. Tuytelaars and L. Van Gool, Surf: Speeded up robust features, European Conf. Computer Vision (2006), pp. 1-14.
8. P. Berkhin, Grouping multidimensional data, in A Survey of Clustering Data Mining Techniques (Springer, 2006).
9. C. M. Bishop, Pattern Recognition and Machine Learning, 1st edn. (Springer, 2006).
10. O. Chapelle, B. Scholkopf and A. Zien, Semi-supervised Learning (MIT Press, 2006).
11. P. Clark and R. Boswell, Rule induction with CN2: Some improvements, European Working Session on Learning on Machine Learning (1991), pp. 151-163.
12. T. Dietterich and G. Bakiri, Solving multi-class problems via ECOC, J. Artif. Intell. Res. 2(1) (1996) 263-286.
13. S. A. Dudani, K. J. Breeding and R. B. McGhee, Aircraft identification by moment invariants, IEEE Trans. Comput. C-26(1) (1977) 39-46.
14. R. Gonzalez and R. Woods, Digital Image Processing, 3rd edn. (Prentice-Hall, 2007).
15. G. Heidemann, Unsupervised image categorization, Image Vis. Comput. 23(10) (2004) 861-876.
16. M. K. Hu, Visual pattern recognition by moment invariants, IEEE Trans. Inform. Theor. 8(2) (1962) 179-187.
17. G. N. Lance and W. T. Williams, Computer programs for hierarchical polythetic classi- fication ("similarity analysis"), Comput. J. 9 (1966) 60-64.
18. J. Li, J. Z. Wang and G. Wiederhold, IRM: Integrated region matching for image retrieval, ACM Int. Conf. Multimedia (2000), pp. 147-156.
19. D. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis. 60(2) (2004) 91-110.
20. J. P. Papa, A. X. Falcão and C. T. N. Suzuki, Supervised pattern classification based on optimum-path forest, Int. J. Imag. Syst. Tech. 19(2) (2009) 120-131.
21. A. Passerini, M. Pontil and P. Frasconi, New results on error correcting output codes of kernel machines, IEEE Trans. Neural Netw. 15(1) (2004) 45-54.
22. O. Pujol, P. Radeva and J. Vitria, Discriminant ECOC: A heuristic method for application dependent design of ECOC, IEEE Trans. Pattern Anal. Mach. Intell. 28(6) (2006) 1007-1012.
23. M. G. Quiles, L. Zhao, F. A. Breve and A. Rocha, Label propagation through neuronal synchrony, IEEE Int. Joint Conf. on Neural Networks (2010), pp. 2517-2524.
24. A. Rocha and S. Goldenstein, From binary to multi-class: Divide to conquer, Int. Conf. Computer Vision Theory and Applications (2009), pp. 323-330.
25. A. Rocha, D. Hauagge, J. Wainer and S. Goldenstein, Automatic fruit and vegetable classification from images, Comput. Electron. Agr. 70(1) (2010) 96-104.
26. Y. Rubner, C. Tomasi and L. J. Guibas, The earth mover's distance as a metric for image retrieval, Int. J. Comput. Vis. 40(2) (2000) 99-121.
27. P. K. Saha, Tensor scale: A local morphometric parameter with applications to computer vision and image processing, Comput. Vis. Image Understand. 99(3) (2005) 384-413.
28. J. Sivic and A. Zisserman, Video google: A text retrieval approach to object matching in videos, IEEE Int. Conf. Computer Vision (2003), pp. 1470-1477.
29. R. Stehling, M. Nascimento and A. Falcão, A compact and effcient image retrieval approach based on border/interior pixel classification, ACM Int. Conf. Information and Knowledge Management (2002), pp. 102-109.
30. R. Torres and A. Falcão, Content-based image retrieval: Theory and applications, Journal of Theoretical and Applied Computing 13(2) (2006) 161-185.
31. R. Torres, A. X. Falcão and L. F. Costa, A graph-based approach for multiscale shape analysis, Pattern Recogn. 37(6) (2004) 1163-1174.
32. E. Valle, Local-descriptor matching for image identification systems, Ph.D. thesis, Universit de Cergy-Pontoise, Cergy-Pontoise, France (2008).
33. M. Weber, Unsupervised learning of models for object recognition, Ph.D. thesis, California Institute of Technology (2000).
34. C. T. Zahn and R. Z. Roskies, Fourier descriptors for plane closed curves, IEEE Trans. Comput. C-21(3) (1972) 269-281.