6,142 research outputs found
Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables
The use of non-parametric methodologies, the introduction of non-financial variables,
and the development of models geared towards the homogeneous characteristics of
corporate sub-populations have recently experienced a surge of interest in the bankruptcy
literature. However, no research on default prediction has yet focused on micro-entities
(MEs), despite such firms’ importance in the global economy. This paper builds the first
bankruptcy model especially designed for MEs by using a wide set of accounts from 1999
to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs
outperform the traditional logistic regression (LR) models. In addition, we also report
that, thanks to the introduction of non-financial predictors related to age, the delay
in filing accounts, legal action by creditors to recover unpaid debts, and the ownership
features of the company, the improvement with respect to the use of solely financial
information is 3.6%, which is even higher than the improvement that involves the use
of the best ANN (2.6%)
Identification of Nonlinear Systems Using Radial Basis Function Neural Network
This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian
function
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
This paper presents a comparative study of two different methods, which are
based on fusion and polar transformation of visual and thermal images. Here,
investigation is done to handle the challenges of face recognition, which
include pose variations, changes in facial expression, partial occlusions,
variations in illumination, rotation through different angles, change in scale
etc. To overcome these obstacles we have implemented and thoroughly examined
two different fusion techniques through rigorous experimentation. In the first
method log-polar transformation is applied to the fused images obtained after
fusion of visual and thermal images whereas in second method fusion is applied
on log-polar transformed individual visual and thermal images. After this step,
which is thus obtained in one form or another, Principal Component Analysis
(PCA) is applied to reduce dimension of the fused images. Log-polar transformed
images are capable of handling complicacies introduced by scaling and rotation.
The main objective of employing fusion is to produce a fused image that
provides more detailed and reliable information, which is capable to overcome
the drawbacks present in the individual visual and thermal face images.
Finally, those reduced fused images are classified using a multilayer
perceptron neural network. The database used for the experiments conducted here
is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database
benchmark thermal and visual face images. The second method has shown better
performance, which is 95.71% (maximum) and on an average 93.81% as correct
recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in
Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11
- 15, 201
Neural Networks for Complex Data
Artificial neural networks are simple and efficient machine learning tools.
Defined originally in the traditional setting of simple vector data, neural
network models have evolved to address more and more difficulties of complex
real world problems, ranging from time evolving data to sophisticated data
structures such as graphs and functions. This paper summarizes advances on
those themes from the last decade, with a focus on results obtained by members
of the SAMM team of Universit\'e Paris
Combining Stream Mining and Neural Networks for Short Term Delay Prediction
The systems monitoring the location of public transport vehicles rely on
wireless transmission. The location readings from GPS-based devices are
received with some latency caused by periodical data transmission and temporal
problems preventing data transmission. This negatively affects identification
of delayed vehicles. The primary objective of the work is to propose short term
hybrid delay prediction method. The method relies on adaptive selection of
Hoeffding trees, being stream classification technique and multilayer
perceptrons. In this way, the hybrid method proposed in this study provides
anytime predictions and eliminates the need to collect extensive training data
before any predictions can be made. Moreover, the use of neural networks
increases the accuracy of the predictions compared with the use of Hoeffding
trees only
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