Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting

Authors Andrea Passerini, Paolo Frasconi, Luc De Raedt



PDF
Thumbnail PDF

File

DagSemProc.05051.8.pdf
  • Filesize: 361 kB
  • 20 pages

Document Identifiers

Author Details

Andrea Passerini
Paolo Frasconi
Luc De Raedt

Cite AsGet BibTex

Andrea Passerini, Paolo Frasconi, and Luc De Raedt. Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)
https://doi.org/10.4230/DagSemProc.05051.8

Abstract

An example-trace is a sequence of steps taken by a program on a given example input. Different approaches exist in order to exploit example-traces for learning, all explicitly inferring a target program from positive and negative traces. We generalize such idea by developing similarity measures betweeen traces in order to learn to discriminate between positive and negative ones. This allows to combine the expressiveness of inductive logic programming in representing knowledge to the statistical properties of kernel machines. Logic programs will be used to generate proofs of given visitor programs which exploit the available background knowledge, while kernel machines will be employed to learn from such proofs.
Keywords
  • Proof Trees
  • Logic Kernels
  • Learning from Traces

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads