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13 results on '"Romay, MG"'

1. Shallow parsing with long short-term memory

Author

Hammerton, J, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Duro, R, Kerre, EE, Leong, HV, Li, Q, Lu, M, Romay, MG, Ventura, D, and Wu, J

Abstract

Applying Artificial Neural Networks (ANNs) to language learning has been an active area of research in connectionism. However much of this work has involved small and/or artificially created data sets, whilst other approaches to language learning are now routinely applied to large real-world datasets containing hundreds of thousands of words or more, thus raising the question of how ANNs might scale-up. This paper describes recent work on shallow parsing of real world texts using a recurrent neural network(RNN) architecture called Long Short-Term Memory (LSTM)(1).

Published
2003

2. Mining key information of web pages

Author

Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, Wang, C, Zhang, GQ, Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, Wang, C, and Zhang, GQ

Published
2005

3. STEF: Personalized trade exhibition recommendation

Author

Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, Guo, XT, Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, and Guo, XT

Published
2005

4. An extended Kth-best approach for fuzzy linear bilevel problems

Author

Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, Zhang, G, Steele, R, Shi, C, Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, Zhang, G, Steele, R, and Shi, C

Published
2005

5. A web-based decision support system for linear bilevel multi-follower problems without shared variables

Author

Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, Shi, CG, Zhang, GQ, Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, Zhang, K, Shi, CG, and Zhang, GQ

Published
2005

6. Using general fuzzy number to handle fuzziness in group decision

Author

Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Duro, R, Kerre, EE, Leong, HV, Li, Q, Lu, M, Romay, MG, Ventura, D, Wu, J, Zhang, GQ, Lu, J, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Duro, R, Kerre, EE, Leong, HV, Li, Q, Lu, M, Romay, MG, Ventura, D, Wu, J, Zhang, GQ, and Lu, J

Published
2003

7. Reducing Dimensionality in Multiple Instance Learning with a Filter Method

Author

Zafra, A., Pechenizkiy, M., Sebastian Ventura, Corchado, E., Romay, Mg, Savio, Am, and Data Mining

Subjects
Learning classifier system, Wake-sleep algorithm, Computer science, business.industry, Active learning (machine learning), Algorithmic learning theory, Competitive learning, Supervised learning, Stability (learning theory), Online machine learning, Multi-task learning, Feature selection, Semi-supervised learning, Machine learning, computer.software_genre, Ensemble learning, Generalization error, Computational learning theory, Unsupervised learning, Artificial intelligence, Instance-based learning, Empirical risk minimization, business, computer, Curse of dimensionality
Abstract

In this article, we describe a feature selection algorithm which can automatically find relevant features for multiple instance learning. Multiple instance learning is considered an extension of traditional supervised learning where each example is made up of several instances and there is no specific information about particular instance labels. In this scenario, traditional supervised learning can not be applied directly and it is necessary to design new techniques. Our approach is based on principles of the well-known Relief-F algorithm which is extended to select features in this new learning paradigm by modifying the distance, the difference function and computation of the weight of the features. Four different variants of this algorithm are proposed to evaluate their performance in this new learning framework. Experiment results using a representative number of different algorithms show that predictive accuracy improves significantly when a multiple instance learning classifier is learnt on the reduced data set.

8. STEF: Personalized trade exhibition recommendation

Author

Guo, XT, Lu, J, Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, and Zhang, K

Published
2005

10. Mining key information of web pages

Author

Wang, C, Lu, J, Zhang, GQ, Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, and Zhang, K

Published
2005

11. An extended Kth-best approach for fuzzy linear bilevel problems

Author

Zhang, G, Lu, J, Steele, R, Shi, C, Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, and Zhang, K

Published
2005

13. An automated fuzzy finite element procedure for frequency response function analysis

Author

Munck, M., David Moens, Desmet, W., Vandepitte, D., Blair, S, Chakraborty, U, Chen, SH, Cheng, HD, Chiu, DKY, Das, S, Denker, G, Duro, R, Romay, MG, Hung, D, Kerre, EE, VaLeong, H, Lu, CT, Lu, J, Maguire, L, Ngo, CW, Sarfraz, M, Tseng, C, Tsumoto, S, Ventura, D, Wang, PP, Yao, X, Zhang, CN, and Zhang, K

Subjects
noise and vibration engineering
Abstract

ispartof: pages:203-206 ispartof: Proceedings of the 8th Joint Conference on Information Sciences pages:203-206 ispartof: 8th Joint Conference on Information Sciences location:Utah, USA date:21 Jul - 26 Jul 2005 status: published

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