Abstract Functional Magnetic Resonance Imaging (fMRI) is presently one of the most
popular techniques for analysing the dynamic states in brain images using various kinds of …

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …

摘要正则化稀疏模型在机器学习和图像处理等领域发挥着越来越重要的作用,
它具有变量选择功能, 可以解决建模中的过拟合等问题. Tibshirani 提出的Lasso …

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 …

This paper proposes a novel hybrid model that integrates the synergy of two superior
classifiers for functional magnetic resonance imaging (fMRI) recognition, namely …

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 …

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 …

Pattern classification has been increasingly used in functional magnetic resonance imaging
(fMRI) data analysis. However, the classification performance is restricted by the high …