Understanding variations (variant & invariant) of classification tasks/targets

Understanding variations (variant & invariant) of classification tasks/targets

There still lacks a certain mechanism to cater for variance in data and a lack of levels of impact brought by variance. We introduce a composite term called learning, where average improvement upon every epoch divided by previous loss value to have a standard reference across our models of differing...

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Bibliographic Details
Main Author: Wan, Tai Fong
Other Authors: Althea Liang
Format: Final Year Project (FYP)
Language:English
Published: Nanyang Technological University 2020
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Online Access:https://hdl.handle.net/10356/138001

Internet

https://hdl.handle.net/10356/138001

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