Border Pairs Method—constructive MLP learning classification algorithm

Ploj, Bojan; Harb, Robert; Zorman, Milan

doi:10.1016/j.neucom.2013.03.026

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…Also, four models with 20 different types of architecture were examined, which are fully listed in Table 3. To find the optimum architecture for ANN models, including maximum number of hidden layers and neurons, the experts' knowledge and the trial and error method are used [24]. Before performing the classification using neural network, first the relationship between independent variables (minimum temperature, average temperature, maximum temperature, and RH) with the dependent variable (frequency of positive cases) was investigated.…”

Section: Neural Networkmentioning

confidence: 99%

The Role of Ambient parameters on Transmission Rates of the COVID-19 Outbreak: A machine learning model

Jamshidnezhad

Hosseini

Marashic

et al. 2021

Preprint

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The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran during 04/03/2020 to 05/05/2020 was gathered from the official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. The proposed ANN model showed accuracy rates of 87.25% and 86.4% in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable; thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

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Section: Neural Networkmentioning

confidence: 99%

The Role of Ambient parameters on Transmission Rates of the COVID-19 Outbreak: A machine learning model

Jamshidnezhad

Hosseini

Marashic

et al. 2021

Preprint

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“…to the high degree of connectivity between the nodes and to the increased nonlinearity of this neural network, its generalization ability is among the best, being able to deal even with noisy and missing data. More recently, paradigms like deep learning offered the MLP an "upgrade" through the Border Pairs Method [40], thus considerably improving its performances. While increasing the number of hidden layers is likely to lead to the improvement of overall performances, potentially revealing key features embedded in the data, adding too many of them tends to dramatically augment the training time.…”

Section: Jinst 11 C12004mentioning

confidence: 99%

A new approach to theoretical investigations of high harmonics generation by means of fs laser interaction with overdense plasma layers. Combining particle-in-cell simulations with machine learning.

Mihailescu

2016

J. Inst.

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Within the past decade, various experimental and theoretical investigations have been performed in the field of high-order harmonics generation (HHG) by means of femtosecond (fs) laser pulses interacting with laser produced plasmas. Numerous potential future applications thus arise. Beyond achieving higher conversion efficiency for higher harmonic orders and hence harmonic power and brilliance, there are more ambitious scientific goals such as attaining shorter harmonic wavelengths or reducing harmonic pulse durations towards the attosecond and even the zeptosecond range. High order harmonics are also an attractive diagnostic tool for the laser-plasma interaction process itself.Particle-in-Cell (PIC) simulations are known to be one of the most important numerical instruments employed in plasma physics and in laser-plasma interaction investigations. The novelty brought by this paper consists in combining the PIC method with several machine learning approaches. For predictive modelling purposes, a universal functional approximator is used, namely a multi-layer perceptron (MLP), in conjunction with a self-organizing map (SOM). The training sets have been retrieved from the PIC simulations and also from the available literature in the field. The results demonstrate the potential utility of machine learning in predicting optimal interaction scenarios for gaining higher order harmonics or harmonics with particular features such as a particular wavelength range, a particular harmonic pulse duration or a certain intensity. Furthermore, the author will show how machine learning can be used for estimations of electronic temperatures, proving that it can be a reliable tool for obtaining better insights into the fs laser interaction physics. K : Data processing methods; Pattern recognition, cluster finding, calibration and fitting methods; Analysis and statistical methods; Simulation methods and programs

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“…The EBP algorithm offered a tool to determine the value of the parameters, while the determination of the optimal number of neurons is still an open problem. Successively, methods have been presented to address both questions [3][4][5][6][7][8], but the EBP paradigm, with an important series of variations, still represents the standard of machine learning. This paradigm consists in finding the minimum of a loss function, which is typically given by the output mean squared error with respect to the target value.…”

Section: Introductionmentioning

confidence: 99%

A Free From Local Minima Algorithm for Training Regressive MLP Neural Networks

Montisci

2024

AAIML

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In this article an innovative method for training regressive MLP networks is presented, which is not subject to local minima. The Error-Back-Propagation algorithm, proposed by WilliamHinton-Rummelhart, has had the merit of favoring the development of machine learning techniques, which has permeated every branch of research and technology since the mid1980s. This extraordinary success is largely due to the black-box approach, but this same factor was also seen as a limitation, as soon more challenging problems were approached. One of the most critical aspects of the training algorithms was that of local minima of the loss function, typically the mean squared error of the output on the training set. In fact, as the most popular training algorithms are driven by the derivatives of the loss function, there is no possibility to evaluate if a reached minimum is local or global. The algorithm presented in this paper avoids the problem of local minima, as the training is based on the properties of the distribution of the training set, or better on its image internal to the neural network. The performance of the algorithm is shown for a well-known benchmark.

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Border Pairs Method—constructive MLP learning classification algorithm

Cited by 10 publications

References 5 publications

The Role of Ambient parameters on Transmission Rates of the COVID-19 Outbreak: A machine learning model

The Role of Ambient parameters on Transmission Rates of the COVID-19 Outbreak: A machine learning model

A new approach to theoretical investigations of high harmonics generation by means of fs laser interaction with overdense plasma layers. Combining particle-in-cell simulations with machine learning.

A Free From Local Minima Algorithm for Training Regressive MLP Neural Networks

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