NUST Institutions Library Catalogue catalog › Details for: TH-SOF-1793-Unsupervised Feature Selection Based on Rough Set Theory Using Direct Dependency Classes (DDC)

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TH-SOF-1793-Unsupervised Feature Selection Based on Rough Set Theory Using Direct Dependency Classes (DDC)

TextPublisher: ISLAMABAD NUST COLLEGE OF EME 2020Subject(s): MS-CSE-16DDC classification: 200 THE

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Item type	Current location	Home library	Shelving location	Call number	Status	Date due	Barcode	Item holds
Computer Files	College of Electrical & Mechanical Engineering (CEME)	College of Electrical & Mechanical Engineering (CEME)	Reference	200 THE (Browse shelf)	Available		TH-SOF-1793

Total holds: 0

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Previous	200 THE TH-SOF-1790-Unsupervised Feature Selection Using Incremental Dependency Classes Based on Rough Set Theory	200 THE TH-SOF-1791-`"Implementation and Secure Extension of Mo Soft Core in FPGA."	200 THE TH-SOF-1792-Quantitative Analysis of Pathological Strips using Machine Learning	200 THE TH-SOF-1793-Unsupervised Feature Selection Based on Rough Set Theory Using Direct Dependency Classes (DDC)	200 THE TH-SOF-1794-Consistency Detection in Software Requirement Specifications Using Natural Language Processing Techniques	200 THE TH-SOF-1795-An incremental approach for calculating dominance-based rough set dependency	200 THE TH-SOF-1796-FE Modelling of porous structures using Selective laser melting	Next

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