In this paper I present issues and results pertaining to goal-directed inductive machine learning, in particular, inductive learning that takes into account the cost of the errors made when the learned concept description is used. Previous work introduced the notion that learning programs should be able to take as input different policies, so that they can learn under different pragmatic consid...

Statistical Relational Learning (SRL) approaches have been developed to learn in presence of noisy relational data by combining probability theory with first order logic. While powerful, most learning approaches for these models do not scale well to large datasets. While advances have been made on using relational databases with SRL models [14], they have not been extended to handle the complex...

We show that the notion of bias in inductive concept learning can be quantified in a way that directly relates to learning performance, and that this quantitative theory of bias can provide guidance in the design of effective learning algorithms. We apply this idea by measuring some common language biases, including restriction to conjunctive concepts and conjunctive concepts with internal disj...

Incremental learning from noisy data presents dual challenges: that of evaluating multiple hypotheses incrementally and that of distinguishing errors due to noise from errors due to faulty hypotheses. This problem is critical in such areas of machine learning as concept learning, inductive programming, and sequence prediction. I develop a general, quantitative method for weighing the merits of ...

Graphs can be seen as a universal language to describe and model diverse set of complex systems data structures. However, efficiently extracting topological information from dynamic graphs is not straightforward task. Previous works have explored variety inductive graph representation learning frameworks, but despite the surge in development, little research deployed these techniques for real-l...

This paper presents a method to induce relational concepts with neural networks using the inductive logic programming system LINUS. Some first-order inductive learning tasks taken from machine learning literature were applied successfully, thus confirming the quality of the hypothesis generated by neural networks.

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories. Alternative teaching approaches are more inductive. Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established. This stu...

A dual-process model for recall is described, which is based on an architecture for the inductive learning of symbolic categories. The dual-process mechanism (generate-and-recognize) is shown to follow directly as a consequence of the inductive learning method used.

Successful inductive learning requires that training data be expressed in a form where underlying regularities can be recognized by the learning system. Unfortunately , many applications of inductive learning| especially in the domain of molecular biology|have assumed that data are provided in a form already suitable for learning, whether or not such an assumption is actually justiied. This pap...