Pedro Domingos
University of Washington, Seattle
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Mining the network value of customers
Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001 – KDD
One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is ...
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Mining high-speed data streams
Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000 – KDD
Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous data streams brings unique opportunities, but also new challenges. This ...
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Mining knowledge-sharing sites for viral marketing
Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002 – KDD
Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, ...
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Reconciling schemas of disparate data sources: a machine-learning approach
ACM SIGMOD Record, vol. 30,no. 2,2001 – SIGMOD_Record
A data-integration system provides access to a multitude of data sources through a single mediated schema. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the ...
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Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach
2001 ACM SIGMOD International Conference on Management of Data, 2001 – SIGMOD
A data-integration system provides access to a multitude of data sources through a single mediated schema. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the ...
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Mining time-changing data streams
Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001 – KDD
Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over ...
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On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning, vol. 29,no. 2-3,1997 – ML
The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it ...
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MetaCost: A General Method for Making Classifiers Cost-Sensitive
Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999 – KDD
Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD problems. ...
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Learning to map between ontologies on the semantic web
Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated information processing. However, because of the Semantic Web's distributed ...
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Trust Management for the Semantic Web
Semantic Web - ISWC 2003, Second International Semantic Web Conference, vol. 2870,2003 – ISWC
Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises
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