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检索条件"机构=Department of Data Science and Machine Learning Computer Science"
3653 条 记 录,以下是3361-3370 订阅
Incentive decision processes
Incentive decision processes
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28th Conference on Uncertainty in Artificial Intelligence, UAI 2012
作者: Reddi, Sashank J. Brunskill, Emma Machine Learning Department Carnegie Mellon University United States Computer Science Department Carnegie Mellon University United States
We consider Incentive Decision Processes, where a principal seeks to reduce its costs due to another agent's behavior, by offering incentives to the agent for alternate behavior. We focus on the case where a princ... 详细信息
来源: 评论
Time series prediction for energy-efficient wireless sensors: Applications to environmental monitoring and video games
Time series prediction for energy-efficient wireless sensors...
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3rd International ICST Conference on Sensor Systems and Software, S-Cube 2012
作者: Le Borgne, Yann-Aël Bontempi, Gianluca Machine Learning Group Computer Science Department Université Libre de Bruxelles Bd Triomphe 1050 Brussels Belgium
Time series prediction techniques have been shown to significantly reduce the radio use and energy consumption of wireless sensor nodes performing periodic data collection tasks. In this paper, we propose an implement... 详细信息
来源: 评论
Sparse LS-SVM two-steps model selection method
Sparse LS-SVM two-steps model selection method
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2012 International Conference on machine learning and Cybernetics, ICMLC 2012
作者: Sun, Binbin Yeung, Daniel S. Department of Computer Science Shenzhen Graduated School Harbin Institute of Technology China Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology China
Least Square Support Vector machine (LS-SVM) converts the hinge loss function of SVM into a least square loss function which simplified the original quadratic programming training method to a linear system solving pro... 详细信息
来源: 评论
Generalized similarity kernels for efficient sequence classification
Generalized similarity kernels for efficient sequence classi...
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12th SIAM International Conference on data Mining, SDM 2012
作者: Kuksa, Pavel P. Khan, Imdadullah Pavlovic, Vladimir Machine Learning Department NEC Laboratories America Inc. Princeton NJ 08540 United States Department of Computer Science Gulf University for Science and Technology Kuwait Department of Computer Science Rutgers University Piscataway NJ 08854 United States
String kernel-based machine learning methods have yielded great success in practical tasks of struc- Tured/sequential data analysis. They often exhibit state-of-the-art performance on tasks such as docu- ment topic el... 详细信息
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Assessing reliability of protein-protein interactions by gene ontology integration
Assessing reliability of protein-protein interactions by gen...
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2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
作者: Montañez, George D. Cho, Young-Rae Machine Learning Department Carnegie Mellon University Pittsburgh PA United States Department of Computer Science Baylor University Waco TX United States
Recent advances in genome-wide identification of protein-protein interactions (PPIs) have produced an abundance of interaction data which give an insight into functional associations among proteins. However, it is kno... 详细信息
来源: 评论
Supervised probabilistic robust embedding with sparse noise  26
Supervised probabilistic robust embedding with sparse noise
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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
作者: Zhang, Yu Yeung, Dit-Yan Xing, Eric P. Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise referred to as sparse noise is quite comm... 详细信息
来源: 评论
Application of genetic algorithms for detecting anomaly in network intrusion detection systems
Application of genetic algorithms for detecting anomaly in n...
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2nd International Conference on computer science and Information Technology, CCSIT 2012
作者: Srinivasa, K.G. Machine Learning Applications Laboratory Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology Bangalore-560 054 India
Intrusion Detection System (IDS) can handle intrusions in computer environments by triggering alerts to help the analysts for taking actions to stop the possible attack or intrusion. But, the IDS make the job of analy... 详细信息
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GNIDS: Rule-based network intrusion detection system using genetic algorithms
GNIDS: Rule-based network intrusion detection system using g...
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作者: Srinivasa, K.G. Pramod, N. Machine Learning Applications Laboratory Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology Bangalore 560 054 India
Detection of intrusions in computer networks has been a growing problem motivating widespread research in computer science to develop better Intrusion Detecting Systems (IDS). The existing IDS have been quite static a... 详细信息
来源: 评论
Factored models for multiscale decision-making in smart grid customers  26
Factored models for multiscale decision-making in smart grid...
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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
作者: Reddy, Prashant P. Veloso, Manuela M. Machine Learning Department Carnegie Mellon University Pittsburgh PA United States Computer Science Department Carnegie Mellon University Pittsburgh PA United States
Active participation of customers in the management of demand, and renewable energy supply, is a critical goal of the Smart Grid vision. However, this is a complex problem with numerous scenarios that are difficult to... 详细信息
来源: 评论
Trajectory-based short-sighted probabilistic planning
Trajectory-based short-sighted probabilistic planning
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26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
作者: Trevizan, Felipe W. Veloso, Manuela M. Machine Learning Department Carnegie Mellon University Pittsburgh PA United States Computer Science Department Carnegie Mellon University Pittsburgh PA United States
Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state ... 详细信息
来源: 评论