Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/42960
Title: Minimax probability machine regression and extreme learning machine applied to compression index of marine clay
Authors: Samui, Pijush
Kim, Dookie
Keywords: Minimax Probability Machine Regression;Regression;Compression Index;Marine Clay;Extreme Learning Machine
Issue Date: Nov-2017
Publisher: NISCAIR-CSIR, India
Abstract: This article uses Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM) for determination of Compression Index (Cc) of marine clay. MPMR is developed in a probabilistic framework. It maximizes the minimum probability of future predictions being within some bound of the true regression function. ELM is the advanced learning algorithm of single-hidden layer feed forward neural network.  Natural moisture content (wn), liquid limit (LL), void ratio (e) and plasticity index (PI) have been used as inputs of MPMR and ELM. The output of MPMR and ELM is Cc. The results of MPMR and ELM have been compared with the regression models. This study gives a powerful tool based on the developed MPMR for determination of Cc of marine clay. 
Page(s): 2350-2356
URI: http://nopr.niscair.res.in/handle/123456789/42960
ISSN: 0975-1033 (Online); 0379-5136 (Print)
Appears in Collections:IJMS Vol.46(11) [November 2017]

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