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 Programme
Overview
Detailed
Programme
| Machine Learning
and Heuristics III (MLHIII) |
CHAIR: ARMANDO VIEIRA
Time: Monday 21st March, 16h30-18h10
| MLHIII-1 |
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| Title: |
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Efficiency Aspects of Neural Network
Architecture Evolution Using Direct and Indirect Encoding |
| Author(s): |
H. Kawasnicka,
M. Paradowski |
| Abstract: |
sing a GA as a NN designing tool deals with
many aspects. We must decide, among others, about: coding
schema, evaluation function, genetic operators, genetic parameters,
etc. This paper focuses on an efficiency of NN architecture
evolution. We use two main approaches for neural network representation
in the form of chromosomes: direct and indirect encoding.
Presented research is a part of our wider study of this problem.
We present the influence of coding schemata on the possibilities
of evolving optimal neural network. |
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| MLHIII-2 |
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| Title: |
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Genetic Algorithm Optimization
of an Artificial Neural Network for Financial Applications |
| Author(s): |
Serge Hayward |
| Abstract: |
Model discovery and performance surface optimization
with genetic algorithm demonstrate profitability improvement
with an inconclusive effect on statistical criteria. The examination
of relationships between statistics used for economic forecasts
evaluation and profitability of investment decisions reveals
that only the ‘degree of improvement over efficient
prediction’ shows robust links with profitability. If
profits are not observable, this measure is proposed as an
evaluation criterion for an economic prediction. Also combined
with directional accuracy, it could be used in an estimation
technique for economic behavior, as an alternative to conventional
least squares. |
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| MLHIII-3 |
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| Title: |
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A Method to Improve Generalization
of Neural Networks: Application to the Problem of Bankruptcy
Prediction |
| Author(s): |
Armando Vieira ,
João C. Neves ,
Bernardete Ribeiro |
| Abstract: |
The Hidden Layer Learning Vector Quantization
is used to correct the predictions of multilayer perceptrons
for classification of high-dimensional data. Corrections are
significant for problems with insufficient training data to
constrain learning. Our method allows the conclusion of a
large number of attributes without compromising the generalization
capabilities of the network. The method is applied to the
problem of bankruptcy prediction with excellent results |
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| MLHIII-4 |
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| Title: |
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An Adaptive Neural System for Financial
Time Series Tracking |
| Author(s): |
A. Dantas,
J.Seixas |
| Abstract: |
In this paper, we present a neural network based
system to generate an adaptive model for financial time series
tracking. This kind of data is quite relevant for data quality
monitoring in large databases. The proposed system uses the
past samples of the series to indicate its future trend and
to generate a corridor inside which the future samples should
lie. This corridor is derived from an adaptive forecasting
model, which makes use of the walk-forward method to take
into account the most recent observations of the series and
bring up to date the values of the neural model parameters.
The model can serve also to manage other time series characteristics,
such as the detection of irregularities. |
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| MLHIII-5 |
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| Title: |
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Probabilistic Artificial Neural
Networks for Malignant Melanoma Prognosis |
| Author(s): |
R. Joshi,
C. Reeves,
C. Johnston |
| Abstract: |
Artificial Neural networks (ANNs) have found
pplications in a wide variety of medical problems and have
proved successful for non-linear regression and classification.
This paper details a novel and flexible probabilistic non-linear
ANN model for the prediction of conditional survival probability
of malignant melanoma patients. Hazard and probability density
functions are also estimated. The model is trained using the
log-likelihood function, and generalisation has been addressed.
Unrestricted by assumptions that are unrealistic or parametric
forms that are difficult to justify, the model thereby attains
advantage over traditional statistical models. Furthermore,
an estimate of the variance-covariance matrix is obtained
using the asymptotic Fisher information matrix. Implemented
in an Excel spreadsheet, the model’s user-friendly design
further adds to its flexibility, with much potential for use
by statisticians as well as researchers. |
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