Authorship Determination Using Letter Pair Frequency Features with Neural Network Classifiers

1995 ◽  
Vol 9 (2) ◽  
pp. 119-124 ◽  
Author(s):  
B. KJELL
Keyword(s):  
Neural Network ◽  
Letter Pair ◽  
Neural Network Classifiers ◽  
Frequency Features

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

scholarly journals Neural network classifiers for images of genetic conditions with cutaneous manifestations

2021 ◽  
pp. 100053 ◽  
Author(s):  
Dat Duong ◽  
Rebekah L. Waikel ◽  
Ping Hu ◽  
Cedrik Tekendo-Ngongang ◽  
Benjamin D. Solomon
Keyword(s):  
Neural Network ◽  
Cutaneous Manifestations ◽  
Neural Network Classifiers

scholarly journals Genome-enabled prediction using probabilistic neural network classifiers

BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Juan Manuel González-Camacho ◽  
José Crossa ◽  
Paulino Pérez-Rodríguez ◽  
Leonardo Ornella ◽  
Daniel Gianola
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Neural Network Classifiers

scholarly journals Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

2018 ◽  
Vol 8 (8) ◽  
pp. 1258 ◽  
Author(s):  
Shuming Jiao ◽  
Zhi Jin ◽  
Chenliang Chang ◽  
Changyuan Zhou ◽  
Wenbin Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Critical Issue ◽  
Compression Process ◽  
Compression Scheme ◽  
Quality Degradation ◽  
Digital Format ◽  
Enormous Amount ◽  
Frequency Features

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

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