In many different classification tasks it is required to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the …

Many underlying relationships among data in several areas of science and engineering, eg,
computer vision, molecular chemistry, molecular biology, pattern recognition, and data …

This paper firstly provides a re-appraisal of the development of techniques for inverting
deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a …

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

How can we generate realistic graphs? In addition, how can we do so with a mathematically
tractable model that makes it feasible to analyze their properties rigorously? Real graphs …

In this paper we introduce the Graph Echo State Network (GraphESN) model, a
generalization of the Echo State Network (ESN) approach to graph domains. GraphESNs …

This chapter provides a general introduction to the main subject matter of this work: multiple
instance or multi-instance learning. The two terms are used interchangeably in the literature …

Relational learning can be described as the task of learning first-order logic rules from
examples. It has enabled a number of new machine learning applications, eg graph mining …

In this paper we present 1BC and 1BC2, two systems that perform naive Bayesian
classification of structured individuals. The approach of 1BC is to project the individuals …

This paper deals with learning first-order logic rules from data lacking an explicit
classification predicate. Consequently, the learned rules are not restricted to predicate …