Combining additive input noise annealing and pattern transformations for improved handwritten character recognition

Alonso-Weber, Juan Manuel; Sesmero, M. Paz; Sanchís, Araceli

doi:10.1016/j.eswa.2014.07.016

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…Alonso-Weber et al [1] followed a similar approach. In addition to the above mentioned deformations, noise was fed into the MLFN by wiping out a proportion of pixels and also adding pixels randomly.…”

Section: Other Approachesmentioning

confidence: 94%

Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine

Noushahr

Ahmadi

Casey

2015

Research and Development in Intelligent Systems XXXII

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Conventional artificial neural networks and convolutional neural networks perform well on the task of automatic handwriting recognition. But, they suffer from long training times and their complex nature. An alternative learning algorithm called Extreme Learning Machine overcomes these shortcomings by determining the weights of a neural network analytically. In this paper, a novel classifier based on Extreme Learning Machine is proposed that achieves competitive accuracy results while keeping training times low. This classifier is called multilayer ensemble Extreme Learning Machine. The novel classifier is evaluated against traditional backpropagation and Extreme Learning Machine on the well-known MNIST dataset. Possible future work on parallel Extreme Learning Machine is shown up.

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Section: Other Approachesmentioning

confidence: 94%

Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine

Noushahr

Ahmadi

Casey

2015

Research and Development in Intelligent Systems XXXII

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“…Some approaches incorporate the clustering algorithms into the learning process, such as artificial neural network (ANN) [1]. The most famous applications of ANN are computer vision and pattern recognition [2], [3].…”

Section: Guest Editorial Big Data Analytics: Risk and Operations Manamentioning

confidence: 99%

Guest Editorial Big Data Analytics: Risk and Operations Management for Industrial Applications

Chan

Choi

Yue

2016

IEEE Trans. Ind. Inf.

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“…This modifies the training instances and allows the dataset to be expanded. As a result, the trained network will improve its ability to generalize and its tolerance to noisy inputs, generating a more robust network [22]. In a general sense, training a network with noise promotes more neurons in the network to contribute effectively to its operation [23].…”

Section: Autoencodersmentioning

confidence: 99%

Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition

et al. 2021

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There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.

show abstract

Combining additive input noise annealing and pattern transformations for improved handwritten character recognition

Cited by 14 publications

References 27 publications

Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine

Fast Handwritten Digit Recognition with Multilayer Ensemble Extreme Learning Machine

Guest Editorial Big Data Analytics: Risk and Operations Management for Industrial Applications

Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition

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