X Li, Y Wang, R Ruiz - IEEE transactions on cybernetics, 2020 - ieeexplore.ieee.org
Feature selection is important in both machine learning and pattern recognition. Successfully selecting informative features can significantly increase learning accuracy and …
One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus …
X Sun, J Park, K Kang, J Hur - 2017 IEEE international …, 2017 - ieeexplore.ieee.org
This paper proposes a novel hybrid model that integrates the synergy of two superior classifiers for functional magnetic resonance imaging (fMRI) recognition, namely …
E Glaab - Briefings in Bioinformatics, 2016 - academic.oup.com
For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression …
The goal of search is to maximize the probability of target detection while covering most of the environment in minimum time. Existing approaches only consider one of these …
N Vlassis, E Glaab - Statistical applications in genetics and molecular …, 2015 - degruyter.com
Complex diseases are often characterized by coordinated expression alterations of genes and proteins which are grouped together in a molecular network. Identifying such …
The AI community has been paying attention to submodular functions due to their various applications (eg, target search and 3D mapping). Learning submodular functions is a …
Y Wang, J Ji, P Liang - Journal of X-ray Science and …, 2016 - content.iospress.com
Pattern classification has been increasingly used in functional magnetic resonance imaging (fMRI) data analysis. However, the classification performance is restricted by the high …