In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. The relational representation of data and background...
Abstract. Despite the considerable success of Inductive Logic Programming, deployed ILP systems still have efficiency problems when applied to complex problems. Several techniques ...
Rui Camacho, Nuno A. Fonseca, Ricardo Rocha, V&iac...
Research over the past several decades in learning logical and probabilistic models has greatly increased the range of phenomena that machine learning can address. Recent work has ...
: This paper is concerned with relational Support Vector Machines, at the intersection of Support Vector Machines (SVM) and relational learning or Inductive Logic Programming (ILP)...
We describe an application of inductive logic programming to transfer learning. Transfer learning is the use of knowledge learned in a source task to improve learning in a related ...
Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richa...
In reinforcement learning problems, an agent has the task of learning a good or optimal strategy from interaction with his environment. At the start of the learning task, the agent...
Tom Croonenborghs, Kurt Driessens, Maurice Bruynoo...
Several activities related to semantically annotated resources can be enabled by a notion of similarity, spanning from clustering to retrieval, matchmaking and other forms of induc...
The management of business processes has recently received a lot of attention. One of the most interesting problems is the description of a process model in a language that allows ...
Mode declarations are a successful form of language bias in explanatory ILP. But, while they are heavily used in Horn systems, they have yet to be similarly exploited in more expre...
Two-way alternating automata were introduced by Vardi in order to study the satisfiability problem for the modal µ-calculus extended with backwards modalities. In this paper, we ...