• Complexity parameters for firs...

Result: Complexity parameters for first order classes

Title:
Complexity parameters for first order classes
Authors:
ARIAS, Marta, KHARDON, Roni
Source:
Machine learning. 64(1-3):121-144
Publisher Information:
Dordrecht: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 29 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Logique ordre 1, First order logic, Lógica orden 1, Programmation logique inductive, Inductive logic programming, Apprentissage requête, Théorie apprentissage, Inductive logic programming, Learning theory, First-order logic, VC-dimension, Query learning
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Center for Computational Learning Systems, Columbia University, New York, NY 10115, United States
Department of Computer Science, Tufts University, Medford, MA 02155, United States
ISSN:
0885-6125
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18079947
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18079947
Database:
PASCAL Archive

Further Information

We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by sets of parameters that are used in the literature and thus they cannot give a complete characterization in terms of learnability with polynomial resources. We then identify an alternative notion of size and a simple set of parameters that are useful for first order Horn Expressions. These parameters are the number of clauses in the expression, the maximum number of distinct terms in a clause, and the maximum number of literals in a clause. Matching lower bounds derived using the Vapnik Chervonenkis dimension complete the picture showing that these parameters are indeed crucial.