All talks will be held in lecture hall H6 at the western end of the main hall.
The time for a talk is 20 minutes plus 5 min discussion.
Tuesday, 2007-09-04 - from 16:00 to 17:45
Dynamical Equilibrium, trajectories study in an economical system. The case of the labor market.
|Presenting Author: Marie Cottrell|
|Authors: Patrick Letrémy, Marie Cottrell, Patrice Gaubert, Joseph Rynkiewicz|
The paper deals with the study of labor market dynamics, and aims
to characterize its equilibriums and
possible trajectories. The theoretical background is the theory of
the segmented labor market. The main idea is that this theory is
well adapted to interpret the observed trajectories, due to the
heterogeneity of the work situations. The Kohonen algorithm is used
to define the segments of the labor market. The trajectories are reconstructed
by means of a non homogeneous Markov model and classified by using a Kohonen algorithm again.
Intraday trading rules based on Self Organizing Maps
|Presenting Author: Marina Resta|
|Authors: Marina Resta|
Working with five minutes data, we have studied a number of trading rules based on the responses of Kohonen's Self Organizing Maps, evaluating the results
with both financial and statistical indicators, as well as by comparison with classical buy and hold strategy. At the current stage our major findings may be summarized as follows: a) Kohonen's maps are helpful to localize profitable intraday patterns, and b) they generally make possible to achieve higher performances than common buy and hold strategy.
Detection of Anomalies and Novelties in Time Series with Self-Organizing Networks
|Presenting Author: Guilherme A. Barreto|
|Authors: Leonardo Aguayo and Guilherme Barreto|
This paper introduces the DANTE project: Detection of Anomalies and Novelties in Time sEries with self-organizing networks. The goal of this project is to evaluate several self-organizing networks in the detection of anomalies/novelties in dynamic data patterns. For this purpose, we first describe three standard clustering-based approaches which uses well-known self-organizing neural architectures, such as the SOM and the Fuzzy ART algorithms, and then present a novel approach based on the Operator Map (OPM) network. The OPM is a generalization of the SOM where neurons are regarded as temporal filters for dynamic patters. The OPM is used to build local adaptive filters for a given nonstationary time series. Non-parametric confidence intervals are then computed for the residuals of the local models and used
as decision thresholds for detecting novelties/anomalies. Computer simulations are carried out to compare the performances of the aforementioned algorithms.
Sleep Spindle Detection by Using Merge Neural Gas
|Presenting Author: Pablo Estevez|
|Authors: Pablo Estevez, Ricardo Zilleruelo-Ramos, Rodrigo Hernandez, Leonardo Causa, Claudio Held|
In this paper the Merge Neural Gas (MNG) model is applied to detect
sleep spindles in EEG. Features are extracted from windows of the
EEG by using short time Fourier transform. The total power spectrum
is computed in six frequency bands and used as input to the MNG
network. The results show that MNG outperforms simple neural gas in
correctly detecting sleep spindles. In addition the temporal
quantization results as well as sleep trajectories are visualized on
two-dimensional maps by using the OVING projection method.