This article presents a new rule discovery algorithm named PLCG that can find simple, robust partial rule models (sets of classification rules) in complex data where it is difficult or impossible to find models that completely account for all the phenomena of interest. Technically speaking, PLCG is an ensemble learning method that learns multiple models via some standard rule learning algorithm...

OBJECTIVES Rule induction is one of the major methods of machine learning. Rule-based models can be easily read and interpreted by humans, that makes them particularly useful in survival studies as they can help clinicians to better understand analysed data and make informed decisions about patient treatment. Although of such usefulness, there is still a little research on rule learning in surv...

Human infants possess powerful learning mechanisms used for the acquisition of language. To what extent are these mechanisms domain specific? One well-known infant language learning mechanism is the ability to detect and generalize rule-like similarity patterns, such as ABA or ABB [Marcus, G. F., Vijayan, S., Rao, S. B., & Vishton, P. M. (1999). Rule learning by seven-month-old infants. Science...

This chapter provides an overview of reinforcement learning and temporal difference learning and relates these topics to the firing properties of midbrain dopamine neurons. First, we review the Rescorla Wagner learning rule and basic learning phenomena, such as blocking, which the rule explains. Then we introduce the basic functional anatomy of the dopamine system and review studies that reveal...

Effective automated graphics generation systems rely on well-established design rules. We are currently exploring how machine learning techniques may be used to acquire design rules automatically. In this paper, we present a model that addresses four fundamental aspects toward applying machine learning to graphics design rule formulation. In particular, learning spaces and learning goals descri...