Multiscale Classi cation is a simple rule-based inductive learning algorithm. It can be applied to any N -dimensional real or binary classi cation problem to successively split the feature space in half to correctly classify the training data. The algorithm has several advantages over existing rulebased and neural network approaches: it is very simple, it learns very quickly, there is no networ...

This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of examples, and study its properties, showing they co...

In the past few years there has been a lot of work lying at the intersection of probability theory, logic programming and machine learning [14, 18, 13, 9, 6, 1, 11]. This work is known under the names of statistical relational learning [7, 5], probabilistic logic learning [4], or probabilistic inductive logic programming. Whereas most of the existing works have started from a probabilistic lear...

The tremendous amounts of linked data available on the web are a valuable resource for a variety of semantic applications. However, these applications often need to face the challenges posed by flawed or underspecified representations. The sheer size of these data sets, being one of their most appealing features, is at the same time a hurdle on the way towards more accurate data because this si...

In this paper we argue that the use of a language with a type system, together with higher-order facilities and functions, provides a suitable basis for knowledge representation in inductive concept learning and, in particular, illuminates the relationship between attribute-value learning and inductive logic programming (ILP). Individuals are represented by closed terms: tuples of constants in ...

Prosodic phrasing is an important component in modern TTS systems, which inserts natural and reasonable breaks into long utterance. This paper reports the study of applying several inductive machine-learning algorithms to prosodic phrasing in unrestricted Chinese texts. Two feature sets are carefully selected considering the effectiveness and reliability of them in practice. Then features and t...

Even though synthesis is a crucial process in engineering design, synthesis knowledge is scarce. Previous studies in the use of machine learning techniques for design domains have concentrated mainly on learning analysis, rather than synthesis, knowledge. This study shows how synthesis knowledge can be acquired by concept formation. The paper evaluates the concept formation program COBWEB, disc...

A extension to the k-nearest neighbor classifier is described in which automatically induced rules are used as binary features, which are active in an instance when the left-hand side of the corresponding rule matches with the instance. The ripper rule induction algorithm is employed to produce the rules. The similarity between a memory instance and a new instance is based on the rules the two ...

In this paper, we are interested in the induction of normal clauses. Several semantics have been proposed for normal programs, we consider here the well-founded semantics, based on a three-valued logics. We have generalized to this semantic the classical constraint: all the positive examples must be covered by the learned program and all the negative ones must be rejected. In the case of induct...

There is a steadily increasing need for autonomous systems that must be able to function with minimal human intervention to detect and isolate faults, and recover from such faults. In this paper we present a novel hybrid Model based and Data Clustering (MDC) architecture for fault monitoring and diagnosis, which is suitable for complex dynamic systems with continuous and discrete variables. The...