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检索条件"机构=Department of Data Science and Machine Learning Computer Science"
3631 条 记 录,以下是3541-3550 订阅
COMPARISON STUDY OF SENSITIVITY DEFINITIONS OF NEURAL NETWORKS
COMPARISON STUDY OF SENSITIVITY DEFINITIONS OF NEURAL NETWOR...
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2007 International Conference on machine learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
作者: CHUN-GUO LI HAI-FENG LI AI-KE YAO NING XU Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Baoding 0710 Department of educational administration Hebei University Baoding 071002 China Department of State Assets Management HeBei Software Institute Baoding 071000 China Industrial and Commercial College Hebei University Baoding 071002 China
This paper compares the sensitivity definitions of neural networks' output to input and weight *** on the essence of the sensitivity definitions, the authors classify these sensitivity definitions into 3 categorie... 详细信息
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A Study On Iterative learning Control With Adjustment Of learning Interval For Monotone Convergence In The Sense Of Sup-Norm
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Asian Journal of Control 2008年 第1期4卷
作者: Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.
It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of... 详细信息
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BiTAM: Bilingual topic AdMixture models for word alignment  21
BiTAM: Bilingual topic AdMixture models for word alignment
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21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
作者: Zhao, Bing Xing, Eric P. Language Technologies Institute School of Computer Science Carnegie Mellon University Machine Learning Department School of Computer Science Carnegie Mellon University
We propose a novel bilingual topical admixture (BiTAM) formalism for word alignment in statistical machine translation. Under this formalism, the parallel sentence-pairs within a document-pair are assumed to constitut... 详细信息
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Scale-free paradigm in yeast genetic regulatory network inferred from microarray data
Scale-free paradigm in yeast genetic regulatory network infe...
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AISB'06: Adaptation in Artificial and Biological Systems
作者: Kontos, Kevin Bontempi, Gianluca ULB Machine Learning Group Computer Science Department Université Libre de Bruxelles 1050 Brussels Belgium
A major challenge of computational biology is the inference of genetic regulatory networks and the identification of their topology from DNA microarray data. Recent results show that scale-free networks play an import... 详细信息
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KFOIL: learning simple relational kernels
KFOIL: Learning simple relational kernels
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21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
作者: Landwehr, Niels Passerini, Andrea De Raedt, Luc Frasconi, Paolo Machine Learning Lab Department of Computer Science Albert-Ludwigs Universität Freiburg Germany Machine Learning and Neural Networks Group Dipartimento di Sistemi e Informatica Università degli Studi di Firenze Florence Italy
A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature sp... 详细信息
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Accelerated training of conditional random fields with stochastic gradient methods  06
Accelerated training of conditional random fields with stoch...
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23rd International Conference on machine learning, ICML 2006
作者: Vishwanathan, S.V.N. Schraudolph, Nicol N. Schmidt, Mark W. Murphy, Kevin P. Statistical Machine Learning National ICT Australia Locked Bag 8001 Canberra ACT 2601 Australia Department of Computer Science University of British Columbia Canada
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer c... 详细信息
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Model-based sequential organization in cochannel speech
Model-based sequential organization in cochannel speech
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作者: Shao, Yang Wang, Deliang IEEE Department of Computer Science and Engineering Center for Cognitive Science Ohio State University Columbus OH 43210-1277 United States Computer Science and Engineering Ohio State University Columbus Department of Computer Science and Engineering Center for Cognitive Science Ohio State University Columbus IEEE Computational Intelligence Society Neural Networks Technical Committee Governing Board of the International Neural Network Society IEEE Signal Processing Society Machine Learning for Signal Processing Technical Committee
A human listener has the ability to follow a speaker's voice while others are speaking simultaneously;in particular, the listener can organize the time-frequency energy of the same speaker across time into a singl... 详细信息
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learning user preferences for sets of objects  06
Learning user preferences for sets of objects
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ICML 2006: 23rd International Conference on machine learning
作者: DesJardins, Marie Eaton, Eric Wagstaff, Kiri L. University of Maryland Baltimore County Computer Science and Electrical Engineering Department 1000 Hilltop Circle Baltimore MD 21250 United States Machine Learning and Instrument Autonomy Group Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena CA 91109 United States
Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as inpu... 详细信息
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A NEW DEFINITION OF REDUCTION IN ROUGH SETS
A NEW DEFINITION OF REDUCTION IN ROUGH SETS
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2006 International Conference on machine learning and Cybernetics(IEEE第五届机器学习与控制论坛)
作者: XIANG-HONG LI SHI-XIN ZHAO NA CHEN QUN-FENG ZHANG Department of Mathematics Shijiazhuang Railway Institute Shijiazhuang City Hebei Province China Department of Computer Science Shijiazhuang Railway Institute Shijiazhuang City Hebei Province C Machine Learning Center School of Mathematics and Computer Science Hebei University Hebei Provinc
This paper proposes a new definition of reduction in rough sets, which follows naturally from the concepts of the degree of similarity and the degree of inconsistency. The new definition is compared to the classical d... 详细信息
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Hyperparameter learning for graph based semi-supervised learning algorithms  06
Hyperparameter learning for graph based semi-supervised lear...
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Proceedings of the 20th International Conference on Neural Information Processing Systems
作者: Xinhua Zhang Wee Sun Lee Statistical Machine Learning Program National ICT Australia Canberra Australia and CSL RSISE ANU Canberra Australia Department of Computer Science National University of Singapore Singapore
Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorit...
来源: 评论