264 research outputs found
Quickest detection in coupled systems
This work considers the problem of quickest detection of signals in a coupled
system of N sensors, which receive continuous sequential observations from the
environment. It is assumed that the signals, which are modeled a general Ito
processes, are coupled across sensors, but that their onset times may differ
from sensor to sensor. The objective is the optimal detection of the first time
at which any sensor in the system receives a signal. The problem is formulated
as a stochastic optimization problem in which an extended average Kullback-
Leibler divergence criterion is used as a measure of detection delay, with a
constraint on the mean time between false alarms. The case in which the sensors
employ cumulative sum (CUSUM) strategies is considered, and it is proved that
the minimum of N CUSUMs is asymptotically optimal as the mean time between
false alarms increases without bound.Comment: 6 pages, 48th IEEE Conference on Decision and Control, Shanghai 2009
December 16 - 1
Fusing image representations for classification using support vector machines
In order to improve classification accuracy different image representations
are usually combined. This can be done by using two different fusing schemes.
In feature level fusion schemes, image representations are combined before the
classification process. In classifier fusion, the decisions taken separately
based on individual representations are fused to make a decision. In this paper
the main methods derived for both strategies are evaluated. Our experimental
results show that classifier fusion performs better. Specifically Bayes belief
integration is the best performing strategy for image classification task.Comment: Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th
International Conference, Wellington : Nouvelle-Z\'elande (2009
Preference fusion and Condorcet's Paradox under uncertainty
Facing an unknown situation, a person may not be able to firmly elicit
his/her preferences over different alternatives, so he/she tends to express
uncertain preferences. Given a community of different persons expressing their
preferences over certain alternatives under uncertainty, to get a collective
representative opinion of the whole community, a preference fusion process is
required. The aim of this work is to propose a preference fusion method that
copes with uncertainty and escape from the Condorcet paradox. To model
preferences under uncertainty, we propose to develop a model of preferences
based on belief function theory that accurately describes and captures the
uncertainty associated with individual or collective preferences. This work
improves and extends the previous results. This work improves and extends the
contribution presented in a previous work. The benefits of our contribution are
twofold. On the one hand, we propose a qualitative and expressive preference
modeling strategy based on belief-function theory which scales better with the
number of sources. On the other hand, we propose an incremental distance-based
algorithm (using Jousselme distance) for the construction of the collective
preference order to avoid the Condorcet Paradox.Comment: International Conference on Information Fusion, Jul 2017, Xi'an,
Chin
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