By Wirth N.

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2002) 634–641 17. : Bayesian Theory. Wiley Series in Applied Probability and Statistics (1994) 18. : Efficient simulation from the multivariate normal and Student tdistributions subject to linear constraints. In: Proceedings of 23rd Symp. Interface. (1991) 571–577 19. : Normalized cuts and image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. (1997) 731–737 Learning Mixtures of Weighted Tree-Unions by Minimizing Description Length Andrea Torsello1 and Edwin R. uk Abstract.

Solving the multiple instance learning with axis-parallel rectangles. Artificial Intelligence 89 (1997) 31–71 11. : Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems 16, Cambridge, MA, MIT Press (2003) 12. : Semi-supervised learning using Gaussian fields and harmonic functions. In: International Conference on Machine Learning. (2003) 13. : Semi-supervised learning on manifolds. Technical Report TR-2002-12, Computer Science Department, The University of Chicago, MA (1994) 14.

1. Annotated images from the Corel database. We would like to automatically match image regions to words in the caption. That is we don’t know the right associations (correspondences) between image features and text features. tion as the process of finding the correct associations between the labels in the caption and the image segments. Knowing the associations allows us to build a translation model that takes as input image features and outputs the appropriate words; see [3] for a detailed description.

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A basic course on compiler principles by Wirth N.

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