101,571 research outputs found
Exploring the number of groups in robust model-based clustering
Producción CientíficaTwo key questions in Clustering problems are how to determine the number of
groups properly and measure the strength of group-assignments. These questions are
specially involved when the presence of certain fraction of outlying data is also expected.
Any answer to these two key questions should depend on the assumed probabilistic-
model, the allowed group scatters and what we understand by noise. With this in
mind, some exploratory \trimming-based" tools are presented in this work together
with their justi cations. The monitoring of optimal values reached when solving a
robust clustering criteria and the use of some "discriminant" factors are the basis for these exploratory tools.Estadística e I
Simulation of an Optional Strategy in the Prisoner's Dilemma in Spatial and Non-spatial Environments
This paper presents research comparing the effects of different environments
on the outcome of an extended Prisoner's Dilemma, in which agents have the
option to abstain from playing the game. We consider three different pure
strategies: cooperation, defection and abstinence. We adopt an evolutionary
game theoretic approach and consider two different environments: the first
which imposes no spatial constraints and the second in which agents are placed
on a lattice grid. We analyse the performance of the three strategies as we
vary the loner's payoff in both structured and unstructured environments.
Furthermore we also present the results of simulations which identify scenarios
in which cooperative clusters of agents emerge and persist in both
environments.Comment: 12 pages, 8 figures. International Conference on the Simulation of
Adaptive Behavio
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
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