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My research interests are mainly in
the fields of biologically
inspired computer vision, statistical pattern
recognition, and machine learning. I am
particularly interested in developing new learning
and classification models. In developing these
models I use methods from computer science (e.g.
artificial neural networks and Bayesian inference), mathematics
(e.g. statistical pattern recognition), engineering
(e.g. signal processing), and physics (e.g. statistical
mechanics). In addition, the inspiration for
constructing learning algorithms comes from human
perception and the way information is processed by the
human visual system.
I am also involved in several medical projects in which
the objective is to analyze physiological data and in particular brain
signals (e.g. EEG and fMRI data) using various pattern recognition
techniques. For example, we are now developing methods to extract
discriminative and predictive features from time sequences and estimate
the information flow among different brain regions using information-theoretic methods. In contrast to traditional techniques,
such as power spectrum, linear correlation and coherence analysis, the
recently introduced entropy-based features can capture higher order
statistics and non-linear dependences.
Recent Publications:
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P. Neskovic, I. Sherman, L. Wu, and L.
N. Cooper.
Learning faces with the BIAS model: on the importance of the sizes
and locations of fixation regions, Neurocomputing,
27, 2915-2922,
2009.
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L. Wu, P.
Neskovic, E. Reyes, E. Festa, and W. Heindel,
Classifying n-back EEG data using entropy and mutual information
features, ESANN, pp. 61-66, 2007.
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L. Wu and
P. Neskovic. Classifying EEG
data into different memory loads across subjects,
Lecture Notes In Computer Science: Artificial Neural Networks - ICANN,
Vol.4669, pp.
149-158, 2007.
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P.
Neskovic, L. Wu, and L. N. Cooper.
Learning by Integrating
Information Within and Across Fixations.
Lecture Notes In Computer Science: Artificial Neural Networks - ICANN,
Vol. 4132, pp. 488-497, 2006.
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J. Wang, P. Neskovic, and L. N.
Cooper.
A minimum Sphere Covering Approach to Pattern Classification.
ICPR,
pp. 433-436, 2006.
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