Information Based Selection of Neural Networks Training Data for S.I. Engine Mapping

2001 ◽  
Author(s):  
Ivan Arsie ◽  
Fabrizio Marotta ◽  
Cesare Pianese ◽  
Gianfranco Rizzo
Keyword(s):  
Neural Networks ◽  
Training Data ◽  
Selection Of

scholarly journals Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

2019 ◽  
Vol 66 (3) ◽  
pp. 363-388
Author(s):  
Serkan Aras ◽  
Manel Hamdi
Keyword(s):  
Neural Networks ◽  
Crude Oil ◽  
Significant Interaction ◽  
Statistical Tests ◽  
Oil Price ◽  
Training Data ◽  
Forecasting Model ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks

2001 ◽  
Vol 5 (5) ◽  
pp. 300-305
Author(s):  
Wimpie D. Nortje ◽  
◽  
Johann E. W. Holm ◽  
Gerhard P. Hancke ◽  
Imre. J. Rudas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Bayesian Networks ◽  
Training Data ◽  
Computational Time ◽  
Bayesian Techniques ◽  
Multiple Networks ◽  
The Neural Networks ◽  
Computational Errors ◽  
Bias Variance ◽  
Selection Of

Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tedious in terms of computational time. It is for this reason that alternatives to Bayesian networks are investigated.

Data Mining in Cyber Threat Analysis – Neural Networks for Intrusion Detection

2016 ◽  
Vol 15 (2) ◽  
pp. 187-196
Author(s):  
Eszter Katalin Bognár
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Neural Networks ◽  
Intrusion Detection ◽  
Training Data ◽  
Detection Accuracy ◽  
Threat Analysis ◽  
Very High ◽  
Computation Speed ◽  
Selection Of

The most important features and constraints of the commercial intrusion detection (IDS) and prevention (IPS) systems and the possibility of application of artificial intelligence and neural networks such as IDS or IPS were investigated. A neural network was trained using the Levenberg-Marquardt backpropagation algorithm and applied on the Knowledge Discovery and Data Mining (KDD)’99 [14] reference dataset. A very high (99.9985%) accuracy and rather low (3.006%) false alert rate was achieved, but only at the expense of high memory consumption and low computation speed. To overcome these limitations, the selection of training data size was investigated. Result shows that a neural network trained on ca. 50,000 data is enough to achieve a detection accuracy of 99.82%.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals Improving Semi-Supervised Learning for Audio Classification with FixMatch

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1807
Author(s):  
Sascha Grollmisch ◽  
Estefanía Cano
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Data Transfer ◽  
State Of The Art ◽  
Training Data ◽  
Audio Classification ◽  
Image Domain ◽  
Full Dataset ◽  
Audio Data

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.

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