In this paper, we investigate the impact of machine learning algorithms in the development of automatic music classification models aiming to capture genres distinctions. The study of genres as bodies of musical items aggregated according to subjective and local criteria requires corresponding inductive models of such a notion. This process can be thus modeled as an example-driven learning task...

Traditionally logic was considered as having two branches: deductive and inductive. However the development of the subject from Frege (1879) up to about 1970 brought about a divergence between deductive and inductive logic. It is argued in this paper that developments in artiicial intelligence in the last twenty or so years (particularly logic programming and machine learning) have created a ne...

Learning design patterns assist the development of effective courses, because patterns capture successful solutions. Pedagogical patterns are commonly created by human cognitive processing in "writer's workshops". Inductive techniques could be used to detect or determine patterns in existing data, or learning designs. This assumes that the learning designs are available in a format that is mach...

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We ar...

This paper introduces a foundation for inductive learning based on the use of higher-order logic for knowledge representation. In particular, the paper (i) provides a systematic individuals-as-terms approach to knowledge representation for inductive learning, and demonstrates the utility of types and higherorder constructs for this purpose; (ii) introduces a systematic way to construct predicat...

In this paper, we discuss a mechanism for transfer learning in the context of inductive process modeling. We begin by describing the dual role of knowledge as a source of model components and structural constraints. Next, we review the task of inductive process modeling and emphasize the effect of domain knowledge on the learning component. We then describe the performance and learning elements...

We explore algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples. First, we consider inductive learning of classification rules. The Reduced Cost Ordering algorithm, a new method for creating a decision list (i.e., an ordered set of rules) is described and compared to a variety of inductive learning approaches. Next, we describe approache...

A novel inductive logic programming system, called Classic’cl is presented. Classic’cl integrates several settings for learning, in particular learning from interpretations and learning from satisfiability. Within these settings, it addresses predictive, descriptive and probabilistic modeling tasks. As such, Classic’cl (C-armr, cLAudien, icl-S(S)at, ICl, and CLlpad) integrates several well-know...

This paper defines a selection problem which selects an appropriate object from a set that is specified by parameters. We discuss inductive learning of selection problems and give a method combining inductive logic programming (ILP) and Bayesian learning. It induces a binary relation comparing likelihood of objects being selected. Our methods estimate probability of each choice by evaluating va...

An investigation of rule learning processes that allow the inclusion of negated features is described. The objective is to establish whether the use of negation in inductive rule learning systems is effective with respect to classification. This paper seeks to answer this question by considering two issues relevant to such systems; feature identification and rule refinement. Both synthetic and ...