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Machine Learning in Complex Networks

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Pages
352 pages
Reading time
13 hours

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Complex networks are explored as powerful tools in machine learning, providing a foundation for techniques in supervised, unsupervised, and semi-supervised learning. The book details a stochastic particle competition method for non-supervised and semi-supervised learning, including an analytical framework for predicting its behavior. It addresses data reliability in semi-supervised contexts, a rarely covered topic. Additionally, a hybrid classification technique is introduced, merging low-level classification with high-level feature extraction from network data, enhancing semantic understanding.

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Machine Learning in Complex Networks, Thiago Christiano Silva, Liang Zhao

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Released
2016
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(Hardcover)
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