The general claims of this paper are twofold: there are challenging problems for Machine Learning in the field of Databases, and the study of these problems leads to a deeper understanding of Machine Learning. To support the first claim, we consider the problem of characterising a database relation in terms of high-level properties, i.e. attribute dependencies. The problem is reformulated to re...

We investigate the problem of learning constraint satisfaction problems from an inductive logic programming perspective. Constraint satisfaction problems are the underlying basis for constraint programming and there is a long standing interest in techniques for learning these. Constraint satisfaction problems are often described using a relational logic, so inductive logic programming is a natu...

In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of ...

This paper investigates the automatic induction of spatial attention from the visual observation of objects manipulated on a table top. In this work, space is represented in terms of a novel observer-object relative reference system, named Local Cardinal System, defined upon the local neighbourhood of objects on the table. We present results of applying the proposed methodology on five distinct...

Inductive learning systems search for regularities that therefore be applied with some assurance to an example describe environmental observations, These systems often use which does not belong to the learning set. In other numeri~~l heu~stics to guide this search, The~ also sele~t words, statistical significance may be used to assess regulantles which are good, or the best, according to certai...

A novel iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The ftrst phase improves the quality of training data through concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training dataset.

Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense reasoning under incomplete information. But, so far, most ILP research has been aimed at Horn progr...

Data mining techniques are studied to discover knowledge from GIS database and remote sensing image data in order to improve land use classification. Two learning granularities are proposed for inductive learning from spatial data, one is spatial object granularity, the other is pixel granularity. The characteristics and application scope of the two granularities are discussed. We also present ...

Current approaches to inductive concept learning suffer from a fundamental difficulty; if a fixed language is chosen in which to represent concepts, then in cases where that language is inappropriate, the new concept may be impossible to describe (and therefore to learn). We suggest a framework for automatically extending the language in which concepts are to be expressed. This framework includ...

Some Experiments with Inductive Logic Programming F. Jacquenet M. Bernard C. Nicolini LIRSIA EURISE CEA Valduc Universit e de Bourgogne Universit e de Saint-Etienne 21011 Dijon Cedex 42023 Saint-Etienne Cedex 2 21120 Is/Tille France France France Abstract Most Intelligent Tutoring Systems (ITS) nowadays integrate some Arti cial Intelligence techniques to improve the quality of the Computer Aide...