Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance Article Swipe

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Ting Wang , Sheng-Uei Guan , Ka Lok Man , Jong Hyuk Park , Hui-Huang Hsu · YOU? · · 2015 · Open Access · · DOI: https://doi.org/10.3390/sym7010053

Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can obtain lower classification error rates than a conventional batch training approach. Yet the reason for that is still not very clear. In this study, the feasibility of IAL is verified by mathematical approaches. Moreover, experimental results derived by IAL neural networks on benchmarks also confirm the mathematical validation.

Type

article

Language

en

Landing Page

https://doi.org/10.3390/sym7010053

PDF

https://www.mdpi.com/2073-8994/7/1/53/pdf?version=1421233169

OA Status

gold

Cited By

1

References

27

Related Works

10

OpenAlex ID

https://openalex.org/W2079997970

Raw OpenAlex JSON