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Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik's Advanced Data Mining and Applications: 8th International PDF

By Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)

ISBN-10: 3642355269

ISBN-13: 9783642355264

ISBN-10: 3642355277

ISBN-13: 9783642355271

This booklet constitutes the refereed lawsuits of the eighth foreign convention on complicated information Mining and functions, ADMA 2012, held in Nanjing, China, in December 2012. The 32 common papers and 32 brief papers awarded during this quantity have been rigorously reviewed and chosen from 168 submissions. they're equipped in topical sections named: social media mining; clustering; computer studying: algorithms and functions; category; prediction, regression and popularity; optimization and approximation; mining time sequence and streaming info; net mining and semantic research; facts mining functions; seek and retrieval; details advice and hiding; outlier detection; subject modeling; and knowledge dice computing.

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Extra info for Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings

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IMS 1999 Reitz Lecture (February 1999) 4. : Stochastic gradient boosting. Tech. , Stanford University (March 1999) 5. : Applied Survival Analysis: Regression Modeling of Time to Event Data, 2nd edn. Wiley-Interscience (2008) 6. : Survival Analysis: A Self-Learning Text, 3rd edn. Springer (2011) 7. : Wikipedia: A Quantitative Analysis. D. thesis, Universidad Rey Juan Carlos (2009) 8. : Survival analysis in open development projects. In: Proceedings of the 2nd International Workshop on Emerging Trends in Free/Libre/Open Source Software Research and Development (FLOSS), Vancouver, Canada, pp.

In this way, the semantic gap can be reduced by using mid-level features to model different content modalities. 1 Bag-of-Features Model In this paper, scale invariant feature transform (SIFT) features are used to model the image components in a document. There have been many other low-level image features such as HOG (histograms of oriented gradients), LBP (local binary Cross-Modal Information Retrieval 17 pattern). The reason for choosing SIFT is for its effectiveness and stability. SIFT features are invariant to rotation, scaling, translation and small distortions [15].

Cong et al. studies, a new probabilistic model was proposed in [14] to investigate mid-level feature correlation between texts and images. In the new model, probabilistic correlations between the mid-level “topics” are considered. Given a query image (text), a SVM classifier can be applied to compute the probability distribution over categories. Most of the models proposed for cross-modal information retrieval are focused on English corpus and the techniques used in studying English can be easily extended to other alphabetic languages.

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Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings by Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)


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