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Parallel SVM Filtering.pdf

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Preview of Parallel SVM Filtering
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📊 Size: 587 KB
📄 Pages: 4 pages
⬇️ Downloads: 98

Summary

A parallel SVM algorithm for large-scale spam filtering is presented, utilizing MapReduce and ontology semantics to improve accuracy and reduce training time. The algorithm distributes and processes subsets of training data across multiple nodes, reducing training time significantly. Ontology-based concepts minimize accuracy degradation when distributing training data amongst SVM classifiers. The parallel SVM is built on the Sequential Minimal Optimization (SMO) algorithm and uses a distributed computing framework, namely MapReduce, with Hadoop's implementation. Experimental results are presented, and future work is discussed.

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Description

A parallel SVM algorithm for large-scale spam filtering is presented, utilizing MapReduce and ontology semantics to improve accuracy and reduce training time.

Technical Information

  • File Format: PDF
  • File Size: 587 KB
  • Pages: 4
  • Language: EN
  • Total Downloads: 98
  • Last Updated: 1 month ago

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This PDF document about Parallel SVM Filtering provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Parallel SVM Filtering.

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