Thomas Hofmann. 2000. Learning probabilistic models of the web. In Research and Development in Information Retrieval. T. Joachims. 1999. Making large-scale SVM learning practical. In B. Scholkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. Home/Search Document Not in Database Summary Related Articles Check |
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Link Analysis Ranking Algorithms Theory And Experiments - Borodin, Roberts.. (2004) (Correct)
....Expectation Maximization (EM) Algorithm of Dempster et al. 15] to compute the authority weights of the pages. Their work is based on the Probabilistic Latent Semantic Analysis framework introduced by Hofmann [24] who proposed a probabilistic alternative to Singular Value Decomposition. Hofmann [25] proposes an algorithm similar to PHITS which also takes into account the text of the documents. These algorithms require specifying in advance the number of factors. Furthermore, it is possible that the EM Algorithm gets stuck in a local maximum, without converging to the true global maximum. ....
Thomas Hofmann. Learning probabilistic models of the web. In Proceedings of the 23rd International Conference on Research and Development in Information Retrieval (ACM SIGIR'00), 2000.
Proceedings of the 9th Conference on Computational Natural.. - Pages Ann Arbor (Correct)
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Thomas Hofmann. 2000. Learning probabilistic models of the web. In Research and Development in Information Retrieval. T. Joachims. 1999. Making large-scale SVM learning practical. In B. Scholkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning.
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