In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the
paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained …

We introduce a novel compositional (recursive) probabilistic model for trees that defines an
approximated bottom-up generative process from the leaves to the root of a tree. The …

This paper presents a family of methods for the design of adaptive kernels for tree-structured
data that exploits the summarization properties of hidden states of hidden Markov models for …

In many application domains, the amount of available data increased so much that humans
need help from automatic computerized methods for extracting relevant information …

KELP is a Java framework that enables fast and easy implementation of kernel functions
over discrete data, such as strings, trees or graphs and their combination with standard …

We introduce pycobra, a Python library devoted to ensemble learning (regression and
classification) and visualisation. Its main assets are the implementation of several ensemble …

In this paper, we show how the Ordered Decomposition DAGs (ODD) kernel framework, a
framework that allows the definition of graph kernels from tree kernels, allows to easily …

The supervised classification of XML documents by structure involves learning predictive
models in which certain structural regularities discriminate the individual document classes …

Tree kernels proposed in the literature rarely use information about the relative location of
the substructures within a tree. As this type of information is orthogonal to the one commonly …

Kernel method is one of the promising approaches to learning with tree-structured data, and
various efficient tree kernels have been proposed to capture informative structures in trees …