Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.12.131
Abstract: Abstract Dropout and DropConnect are useful methods to prevent multilayer neural networks from overfitting. In addition, it turns out that these tools can also be used to estimate the stability of networks regarding disturbances. Prototype… read more here.
Keywords: vector; classification; variants dropconnect; non linear ... See more keywords
Predictive modelling of injury severity in bicycle–motor vehicle collisions utilizing learning vector quantization: a case study of Britain’s cycling capital
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Crashworthiness"
DOI: 10.1080/13588265.2019.1701361
Abstract: Abstract Cambridge is truly known as Britain’s cycling capital but this has all been achieved without any real cycling infrastructure. Therefore, safety concern is the persistent barrier to cycling in Cambridge and its reputation is… read more here.
Keywords: cycling capital; vehicle; learning vector; injury ... See more keywords
Development of Adaptive Fuzzy-Neuro Generalized Learning-Vector Quantization Using PI Membership Function (AFNGLVQ-PI)
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3056021
Abstract: In a real-world environment, there are several difficult obstacles to overcome in classification. Those obstacles are data overlapping and skewness of data distribution. Overlapping data occur when many data from different classes overlap with each… read more here.
Keywords: vector quantization; learning vector; generalized learning; afnglvq ... See more keywords
A Modular and Reconfigurable Pipeline Architecture for Learning Vector Quantization
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Circuits and Systems I: Regular Papers"
DOI: 10.1109/tcsi.2018.2804946
Abstract: Learning vector quantization (LVQ) neural networks have already been successfully applied for image compression and object recognition. In this paper, we propose a modular and reconfigurable pipeline architecture (MRPA) for LVQ. The MRPA consists of… read more here.
Keywords: vector; pipeline architecture; reconfigurable pipeline; learning vector ... See more keywords