The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The interpretive process is considered as an intrinsic part in OSHAM and is carried...

One of the diicult problems in the area of explanation based learning is the utility problem ; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the utility of learned rules. The rst technique is to combine EBL with inductive learning techniques to learn a better set of control rules; the second tec...

We present context-sensitive Multiple Task Learning, or csMTL as a method of inductive transfer embedded in the well known WEKA machine learning suite. csMTL uses a single output neural network and additional contextual inputs for learning multiple tasks. Inductive transfer occurs from secondary tasks to the model for the primary task so as to improve its predictive performance. The WEKA multi-...

This chapter presents a logical analysis of induction. Contrary to common approaches to inductive logic that treat inductive validity as a real-valued generalisation of deductive validity, we argue that the only logical step in induction lies in hypothesis generation rather than evaluation. Inspired by the seminal paper of Kraus, Lehmann and Magidor [Kraus et al., 1990] we analyse the logic of ...

A new inductive learning system, Lab (Learning for ABduction), is presented which acquires abductive rules from a set of training examples. The goal is to nd a small knowledge base which, when used abductively, diagnoses the training examples correctly and generalizes well to unseen examples. This contrasts with past systems that inductively learn rules that are used deductively. Each training ...

We design learning algorithms for synthesizing invariants using Horn implication counterexamples (Horn-ICE), extending the ICE-learning model. In particular, we describe a decision-tree learning algorithm that learns from Horn-ICE samples, works in polynomial time, and uses statistical heuristics to learn small trees that satisfy the samples. Since most verification proofs can be modeled using ...

This paper describes a domain where the application of machine learning, speciically inductive learning, could have enormous positive impact. The domain possesses attributes that would indicate that in-ductive learning would easily succeed for this domain. In particular, data for this domain are abundant. In spite of this, numerous machine learning methods { both inductive and otherwise { have ...

This paper reports a study to identify static software reuse potential metrics that can be used to classify C source code into reusable and non-reusable classes. The techniques used exploit a decision tree inductive machine learning and rough sets theory. The results we obtained show that the former technique, as implemented by C4.5, produces a much more accurate set of classification rules tha...

With the resurgence of interest in neural networks, representation learning has re-emerged as a central focus in artificial intelligence. Representation learning refers to the discovery of useful encodings of data that make domain-relevant information explicit. Factorial representations identify underlying independent causal factors of variation in data. A factorial representation is compact an...

This paper presents KReator, a versatile integrated development environment for probabilistic inductive logic programming currently under development. The area of probabilistic inductive logic programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning in relational or first-order representations of knowledge. In the past ten years the commu...