scholarly journals Mathematical model of bank scoring in conditions of insufficient data

2021 ◽  
Vol 284 ◽  
pp. 04014
Author(s):  
Vladimir Mosin ◽  
Anton Abashkin ◽  
Olga Yusupova
Keyword(s):  
Mathematical Model ◽  
Object Classification ◽  
Classification Problem ◽  
Bayesian Classifier ◽  
Insufficient Data ◽  
Naïve Bayesian Classifier ◽  
Classic Problem ◽  
Dense Set ◽  
Difficult Cases ◽  
Poor Class

Recently, different methods of object classification using training datasets is actually. One of these methods is naive Bayesian classifier. Class of objects can consist of low number of elements. Such class is called poor class. In this paper we consider classification problem in poor class. Logical classifier doesn’t work in this case. Metric classifier can give good results if and only if there are quite dense set of metrically nearby classified objects in neighborhood of the considering object. Bayesian classifier reevaluates all hypotheses about belonging of the object to certain class. Therefore, Bayesian classifier can solve this classification problem. For example, we considered classic problem of bank scoring. This scoring is based on two criteria. Classified object has two belonging hypotheses. We can apply such reasoning for more difficult cases.

Deep Convolutional Neural Network for Object Classification

2021 ◽  
pp. 317-343
Author(s):  
Amira Ahmad Al-Sharkawy ◽  
Gehan A. Bahgat ◽  
Elsayed E. Hemayed ◽  
Samia Abdel-Razik Mashali
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computational Models ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Object Classification ◽  
Classification Problem ◽  
Deep Convolutional Neural Network ◽  
Object Appearance

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.

A HIGHER ORDER BAYESIAN NEURAL NETWORK WITH SPIKING UNITS

1996 ◽  
Vol 07 (02) ◽  
pp. 115-128 ◽  
Author(s):  
ANDERS LANSNER ◽  
ANDERS HOLST
Keyword(s):  
Neural Network ◽  
Real World ◽  
Higher Order ◽  
Bayesian Classifier ◽  
Bayesian Neural Network ◽  
Naïve Bayesian Classifier ◽  
Naive Bayesian ◽  
Naive Bayesian Classifier ◽  
Telephone Exchange ◽  
Bayesian Confidence Propagation

We treat a Bayesian confidence propagation neural network, primarily in a classifier context. The onelayer version of the network implements a naive Bayesian classifier, which requires the input attributes to be independent. This limitation is overcome by a higher order network. The higher order Bayesian neural network is evaluated on a real world task of diagnosing a telephone exchange computer. By introducing stochastic spiking units, and soft interval coding, it is also possible to handle uncertain as well as continuous valued inputs.

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