Normal Forms for Inductive Logic Programming

@inproceedings{Flach1997NormalFF,
  title={Normal Forms for Inductive Logic Programming},
  author={Peter A. Flach},
  booktitle={ILP},
  year={1997}
}
In this paper we study induction of unrestricted clausal theories from interpretations. First, we show that in the propositional case induction from complete evidence can be seen as an equivalence-preserving transformation from DNF to CNF. From this we conclude that induction is essentially a process of determining what is false in the domain of discourse. We then proceed by investigating dual normal forms for evidence and hypotheses in predicate logic. We define evidence nonnal form (ENF… 
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
TLDR
The chapter outlines the main current research directions which have been strongly influenced by recent developments in data mining and challenging real-life applications, and gives an accessible introduction to the above topics.
From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases
  • Peter A. Flach
  • Computer Science
    Transactions and Change in Logic Databases
  • 1998
This chapter aims at demonstrating that inductive logic programming (ILP), a recently established subfield of machine learning that induces first-order clausal theories from examples, combines very
Clausal Discovery
TLDR
CLAUDIEN is an inductive logic programming engine that fits in the descriptive data mining paradigm and employs a novel declarative bias mechanism to define the set of clauses that may appear in a hypothesis.
Database Dependency Discovery: A Machine Learning Approach
TLDR
The algorithms in this paper are designed such that they can easily be generalised to other kinds of dependencies, and the bottom-up algorithm is the most efficient of the three, and also outperforms other algorithms from the literature.
The logic of learning: a brief introduction to Inductive Logic Programming
TLDR
The aim of concept learning is to discover, from a given set of pre-classified examples, a set of classification rules with high predictive power, so-called attribute-value languages have sufficient representational power.
Learning Rule Sets
TLDR
This chapter presents approaches that construct sets of rules that can be constructed by iterative usage of the algorithms for constructing single rules presented in Chap.
Cooking up integrity constraints with PRIMUS (preliminary report)
In this report we describe the main ideas underlying PRIMUS, a system for discovery of first-order integrity constraints guided by heuristics, currently under development.
Reinventing Machine Learning with ROC Analysis
TLDR
Receiver Operating Characteristics is used in medical data analysis to study the effect of varying the threshold on the numerical outcome of a diagnostic test and in response to classification tasks with skewed class distributions or misclassification costs.

References

SHOWING 1-10 OF 11 REFERENCES
Inductive Constraint Logic
TLDR
A novel approach to learning first order logic formulae from positive and negative examples is presented, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples.
Induction in logic
TLDR
An attempt is made to build a unifying framework for the logical aspects of inductive machine learning and data mining, and allows to give in logical terms precise definitions of what is meant by deduction, induction, generalization, specialization, and their relations.
Reasoning with Models
Structure Identification in Relational Data
Exact learning via the Monotone theory
  • N. Bshouty
  • Computer Science
    Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science
  • 1993
TLDR
The Monotone theory is developed that proves any boolean function is learnable as decision tree or as DNF or as CNF, and the first result solves the open problem of the learnability of decision trees.
Fundamentals of logic design and switching theory
Multiple Predicate Learning
The CN 2 induction algorithm
  • Machine Learning
  • 1989
Van Laer . Inductive Constraint Logic
  • Proc . 6 th Workshop on Algorithmic Learning Theory
  • 1995
...
...