Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Support Vector Data Description
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
GATES: A Grid-Based Middleware for Processing Distributed Data Streams
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Online clustering of parallel data streams
Data & Knowledge Engineering
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
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Distributed data streams mining is increasingly demanded in most extensive application domains, like web traffic analysis and financial transactions. In distributed environments, it is impractical to transmit all data to one node for global model. It is reasonable to extract the essential parts of local models of subsidiary nodes, thereby integrating into the global model. In this paper we proposed an approach SVDDS to do this model integration in distributed environments. It is based on SVM theory, and trades off between the risk of the global model and the total transmission load. Our analysis and experiments show that SVDDS obviously lowers the total transmission load while the global accuracy drops comparatively little.