In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies between them. Greedy search prevents current inductive machine learning algorithms to detect signiicant dependencies between the attributes. Recently, Kira and Rendell developed the RELIEF algorithm for estimating the quality of attributes that...

This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in the following subtasks: rule construction, body construction, and feature construction. Each of these subtasks may have its own declarative bias, search strategies, and heuristics. In particular, we ...

This paper focuses on a logical model of induction, and specifically of the common machine learning task of inductive concept learning (ICL). We define an inductive derivation relation, which characterizes which hypothesis can be induced from sets of examples, and show its properties. Moreover, we will also consider the problem of communicating inductive inferences between two agents, which cor...

One of the important problems in integrating inductive learning algorithms with problem-solving systems is determining how they communicate. It is well-known that inductive algorithms are sensitive to the vocabulary with which examples of a concept are described. It is also known that a vocabulary can be acceptable for problem-solving but cause the inductive algorithm to learn slowly or inaccur...

Top-down induction of decison trees (TDIDT) is a very popular machine learning technique. Up till now, it has mainly used for propositional learning, but seldomly for relational learning or inductive logic programming. The main contribution of this paper is the introduction of logic decision trees, which make it possible to use TDIDT in inductive logic programming. An implementation of this top...

The inductive approach to teaching and learning has a long heritage. Getting students to engage in deep learning through critical thinking, problem solving, and discovery are not new goals for education. The Socratic method, dating back to the early Greeks, emphasizes the importance of inductive reasoning and dialogue in the teaching process (Gilstrap et al. 1975). Bruner (1960, 1962, 1966) emp...

One obstacle to wider use of inductive learning algorithms in problem-solving systems is the sensitivity of the algorithms to the way in which examples of the concept are represented. Humans normally decide how the examples will be represented, so success in incorporating inductive learning algorithms varies from person to person. Constructive induction reduces, but does not eliminate, this sen...

The development of bar-code technology provided accurate and large market databases for researchers who deal with datasets. Since the data is large both in dimension and size, most exploratory analysis techniques of statistics are not appropriate for such tasks. In this paper, we describe a high-level algorithm, and the application of it on a large basket data, extracted from the database of a ...

of the Dissertation Inductive Learning of Phonotactic Patterns

External representation is the use of the physical world for cognitive ends, the enlargement of the mechanisms of representation to include the actionperception cycle. It has recently been observed that such representation is pervasive in human activity in both pragmatic and more abstract tasks. It is argued here that by forcing an artificial learning system to off-load all of its representatio...