Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Distributed multivariate regression using wavelet-based collective data mining
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Distributed clustering using collective principal component analysis
Knowledge and Information Systems
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Building predictors from vertically distributed data
CASCON '04 Proceedings of the 2004 conference of the Centre for Advanced Studies on Collaborative research
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Clustering distributed data streams in peer-to-peer environments
Information Sciences: an International Journal
IEEE Communications Magazine
Metastructural facets of granular computing
International Journal of Knowledge Engineering and Soft Data Paradigms
An agent-based framework for distributed learning
Engineering Applications of Artificial Intelligence
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Identifying characteristics of seaports for environmental benchmarks based on meta-learning
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Toward the scalability of neural networks through feature selection
Expert Systems with Applications: An International Journal
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We address the problem of prediction of data that is vertically partitioned, that is where local sites hold some of the attributes of all of the records. This situation is natural when data is collected by channels that are physically separated. For distributed prediction, we show that a technique called attribute ensembles is simple, predicts almost as well as a centralized predictor, reduces the amount of communication required, distributes computation and data access well, and allows each local site to keep its raw data private. We show how to extend attribute ensembles to data that is partitioned both horizontally and vertically.