References - Multimedia Database Retrieval: Technology and Applications - page 330

40. Wu, K., Yap, K.-H.: An efficient radial basis function network approach for content-based
image retrieval. IEEE International Conference on Acoustics, Speech, and Signal Processing
3 (2004)
41. Poggio, T., Girosi, F.: A theory of networks for approximation and leaning. Technical Report
A.I. Memo No. 1140. Massachusetts Institute of Technology (1989)
42. Park, J., Sandberg, I. W.: Universal approximation using radial-basis function networks.
Neural Computation 3 246-257 (1991)
43. Broomhead, D. S., Lowe, D.: Multivariable functional interpolation and adaptive networks.
Complex Syst. 2 321-355 (1988)
44. Chen, S., Grant, P.M., Cowan, C.F.N.: Orthogonal least squares algorithm for training multi-
output radial basis function networks. IEE Proc. Part F 1 39 378-384 (1992)
45. Moody, J., Darken, C. J.: Fast learning in networks of locally-tuned processing units. Neural
Computation 1 281-294 (1989)
46. Chen, S., Cowan, C. F. N., Grant, P. M.: Orthogonal least squares learning algorithm for radial
basis function networks. IEEE Trans. on Neural Networks 2 (1991)
47. Zhang, R., Guan, L.: A collaborative Bayesian image retrieval framework. IEEE International
Conference on Acoustics, Speech and Signal Processing (2009)
48. Zhang, R., Guan, L.: Multimodal image retrieval via Bayesian information fusion. IEEE
International Conference on Multimedia and Expo (2009)
49. Tong, S., Chang, E.: Support vector machine active learning for image retrieval. Proceedings
of the ninth ACM international conference on Multimedia 107-118 (2001)
50. Zitnick, C.: Computing Conditional Probabilities in Large Domains by Maximizing Renyi's
Quadratic Entropy. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA (2003)
51. Rui, Y.: Efficient indexing, browsing and retrieval of image/video content. PhD Thesis,
University of Illinois (1999)
52. Yan, R., Hauptmann, A., Jin, R.: Multimedia search with pseudo-relevance feedback.
International Conference on Image and Video Retrieval. 238-247 (2003)
53. Yan, R., Hauptmann, A., Jin, R.: Negative pseudo-relevance feedback in contentbased
video retrieval. Proceedings of the Eleventh ACM International Conference on Multimedia.
343-346 (2003)
54. Kennedy, L. S., Chang, S. F.: A reranking approach for context-based concept fusion in
video indexing and retrieval. ACM International Conference on Image and Video Retrieval
(Amsterdam, The Netherlands). 333-340 (2007)
55. Wu, Y., Tian, Q., Huang, T. S.: Discriminant-EM algorithm with application to image
retrieval. IEEE Conference on Computer Vision and Pattern Recognition (South Carolina).
222-227 (2000)
56. Hady, M. F. A., Schwenker, F.: Semi-supervised learning. Handbook of Neural Information
Processing. Berlin/Heidelderg: Springer-Verlag. 215-239 (2013)
57. Joachims, T.: Transductive inference for text classification using support vector machines.
International Conference on Machine Learning (Bled, Slovenia). 200-209 (1999)
58. Wang, L., Chan, K., Zhang, Z.: Bootstrapping SVM active learning by incorporating
unlabelled images for image retrieval. IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (Wisconsin). 629-634 (2003)
59. Rudinac, S., Larson, M., Hanjalic, A.: Exploiting visual reranking to improve pseudo-
relevance feedback for spoken-content-based video retrieval. International Workshop on
Image Analysis forMultimedia Interactive Services (London, UK). 17-20 (2009)
60. Carbonell, J. G., Yang, Y., Frederking, R. E., Brown, R. D., Geng, Y., Lee, D.: Translingual
information retrieval: a comparative evaluation. International Joint Conference on Artificial
Intelligence (Aichi, Japan) (1997)
61. Muneesawang, P., Guan, L., Automatic machine interactions for content-based image retrieval
using a self-organizing tree map architecture. IEEE Transactions on Neural Network 13
821-834 (2002)