Yamanishi, K. (1997). Distributed cooperative bayesian learning strategies. Proceedings of COLT 97 (pp. 250--262). New York: ACM. Home/Search Document Not in Database Summary Related Articles Check |
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Distributed Clustering Using Collective Principal.. - Kargupta, Huang.. (2001) (11 citations) (Correct)
....storage, access, and analysis. The ability of various organizations to collect, store, and retrieve huge amounts of data has necessitated the development of algorithms that can extract useful information from these databases. KDD addresses this issue. Distributed knowledge discovery (DKD) [10, 13, 20, 22, 25, 32, 37, 50, 55] takes KDD to a new platform. It embraces the growing trend of merging computation with communication and explores all facets of the KDD process in the context of the emerging distributed computing environments. DKD accepts the fact that data may be inherently distributed among di erent loosely ....
K. Yamanishi. Distributed cooperative Bayesian learning strategies. In Proceedings of COLT 97, pages 250-262, New York, 1997. ACM.
Collective Data Mining: A New Perspective Toward.. - Kargupta, Byung-Hoon, al (1999) (20 citations) (Correct)
....in [14] This technique is similar to meta learning. However this approach is particularly designed for inducing descriptive data model from the predictions of black box classi ers learned in a distributed environment. The Distributed cooperative Bayesian learning approach was developed in [42]. This technique considers homogeneous data sets. In this approach di erent Bayesian agents estimate the parameters of the target distribution and a population learner combines the outputs of those Bayesian models. The agents and the population learner are based on a probabilistic version of the ....
K. Yamanishi. Distributed cooperative bayesian learning strategies. In Proceedings of COLT 97, pages 250-262, New York, 1997. ACM. 38
Collective Data Mining: A New Perspective Toward Distributed.. - Kargupta, al (1999) (20 citations) (Correct)
....scalability. The authors report on a PADMA implementation for unstructured text mining but note that the architecture is not domain specific. There are several examples of agent based systems for information discovery on the World Wide Web (Lesser, 1998; Menczer Belew, 1998; Moukas, 1996) In (Yamanishi, 1997) the author presents two models of distributed Bayesian learning. Both models employ distributed agent learners, each of which observes a sequence of examples and produces an estimate of the parameter specifying the target distribution, and a population learner, which combines the output of the ....
Yamanishi, K. (1997). Distributed cooperative bayesian learning strategies. In Proceedings of COLT 97 (pp.
Distributed Multivariate Regression Using Wavelet-based.. - Hershberger, Kargupta (1999) (2 citations) (Correct)
....using some criterion and some number of the top ranked rules are selected to form the rule set. In (Lam Segre, 1997) the authors extend efforts to automatically produce a Bayesian belief network from discovered knowledge by developing a distributed approach to this exponential time problem. In (Yamanishi, 1997) the author presents two models of distributed Bayesian learning. Both models employ distributed agent learners, each of which observes a sequence of examples and produces an estimate of the parameter specifying the target distribution, and a population learner, that combines the output of the ....
Yamanishi, K. (1997). Distributed cooperative bayesian learning strategies. In Proceedings of COLT 97 (pp.
Collective Data Mining From Distributed.. - Kargupta.. (1998) (1 citation) (Correct)
....The authors report on a PADMA implementation for unstructured text mining but note that the architecture is not domain specific. There are several examples of agent based systems for information discovery on the World Wide Web (Lesser others 1998) Menczer Belew 1998) Moukas 1996) In (Yamanishi 1997) the author presents two models of distributed Bayesian learning. Both models employ distributed agent learners, each of which observes a sequence of examples and produces an estimate of the parameter specifying the target distribution, and a population learner, which combines the output of the ....
Yamanishi, K. 1997. Distributed cooperative bayesian learning strategies. In Proceedings of COLT 97, 250-- 262. New York: ACM.
A New Distributed Data Mining Model Based on Similarity - Tao Li Computer (2003) (1 citation) (Correct)
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Yamanishi, K. (1997). Distributed cooperative bayesian learning strategies. Proceedings of COLT 97 (pp. 250--262). New York: ACM.
Privacy-Preserving Bayesian Network Structure Computation on.. - Wright, Yang (2004) (1 citation) (Correct)
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K. Yamanishi. Distributed cooperative Bayesian learning strategies. Information and Computation, 150(1):22-56, 1999.
Distributed Clustering with Limited Knowledge Sharing - Ghosh, Merugu (Correct)
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K. Yamanishi. Distributed cooperative Bayesian learning strategies. Information and Computation, 150:22--56, 1998.
A Probabilistic Approach to Privacy-sensitive Distributed.. - Srujana Merugu And (2003) (1 citation) (Correct)
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K. Yamanishi. Distributed cooperative Bayesian learning strategies. Information and Computation, 150:22-- 56, 1998.
Privacy-preserving Distributed Clustering using Generative.. - Srujana Merugu And (2003) (4 citations) (Correct)
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K. Yamanishi. Distributed cooperative Bayesian learning strategies. Information and Computation, 150:22--56, 1998.
A Probabilistic Approach to Privacy-sensitive Distributed.. - Srujana Merugu And (2003) (1 citation) (Correct)
No context found.
K. Yamanishi. Distributed cooperative Bayesian learning strategies. Information and Computation, 150:22-- 56, 1998.
Distributed Data Mining Bibliography - Hillol (Correct)
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K. Yamanishi. Distributed Cooperative Bayesian Learning Strategies. In Proceedings of COLT 97, pages 250--262, New York, NY, 1997. ACM.
Distributed Clustering Using Collective Principal.. - Kargupta, Huang.. (1999) (11 citations) (Correct)
No context found.
K. Yamanishi. Distributed cooperative Bayesian learning strategies. In Proceedings of COLT 97, pages 250-262, New York, 1997. ACM.
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