Titlebook: Compression Schemes for Mining Large Datasets; A Machine Learning P T. Ravindra Babu,M. Narasimha Murty,S.V. Subrahman Book 2013 Springer-V

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Root494

https://doi.org/10.1007/978-3-319-77926-3arious aspects of compression schemes both in abstract sense and as practical implementation. We provide a brief summary of content of each chapter of the book and discuss its overall organization. We provide literature for further study at the end.

污秽

https://doi.org/10.1057/9780230210660fication happens to be a fitness function. We provide a few application scenarios in data mining. We provide theoretical discussions on the scheme. Bibliographic notes provide a brief discussion on important relevant references. A list of references is provided in the end.

SSRIS

https://doi.org/10.1007/978-3-030-39037-2 essence, the approach emphasizes exploitation of domain knowledge in mining large datasets, which in the present case results in significant compaction in the data and multiclass classification. We provide a discussion on relevant literature and a list of references at the end of the chapter.

祖先

Introduction,arious aspects of compression schemes both in abstract sense and as practical implementation. We provide a brief summary of content of each chapter of the book and discuss its overall organization. We provide literature for further study at the end.

SPALL

Run-Length-Encoded Compression Scheme,fication happens to be a fitness function. We provide a few application scenarios in data mining. We provide theoretical discussions on the scheme. Bibliographic notes provide a brief discussion on important relevant references. A list of references is provided in the end.

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