A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Land-cover mapping in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

We introduce propagation kernels, a general graph-kernel framework for efficiently
measuring the similarity of structured data. Propagation kernels are based on monitoring …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

In this paper we propose a discriminant learning framework for problems in which data
consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we …

Many real world graphs, such as the graphs of molecules, exhibit structure at multiple
different scales, but most existing kernels between graphs are either purely local or purely …

The design of low-level image features is critical for computer vision algorithms. Orientation
histograms, such as those in SIFT~\cite {Lowe2004Distinctive} and HOG~\cite …

The advantages of discriminative learning algorithms and kernel machines are combined
with generative modeling using a novel kernel between distributions. In the probability …

In this study, heartbeat time series are classified using support vector machines (SVMs).
Statistical methods and signal analysis techniques are used to extract features from the …

Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement
in deep learning field and their applications are quickly spreading in multi-cross-domains …