DagSemProc.05051.8.pdf
- Filesize: 361 kB
- 20 pages
Document
Kernels on Prolog Proof Trees:Statistical Learning in the ILP Setting
Authors
-
Part of:
Volume:
Dagstuhl Seminar Proceedings, Volume 5051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-01-19
Cite As Get 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.
Subject Classification
Keywords
- Proof Trees
- Logic Kernels
- Learning from Traces
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads