Cognitive flexibility is the ability to adapt to changing tasks or problems. To test whether cognitive flexibility is a coherent cognitive capacity in young children, we tested 3- to 5-year-olds' performance on two forms of task switching, rule-based (Three Dimension Changes Card Sorting, 3DCCS) and inductive (Flexible Induction of Meaning-Animates and Objects, FIM-Ob and FIM-An), as well as te...

This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer s...

background and purpose: cervical cancer is the most prevalent cancer among women in the world. cervical cancer is no symptoms and can be treated if diagnosed in the first stage of the disease. the aim of this study was to survey the affecting factors of the pap smears test on perceptual factors, enabling and reinforcing (pen-3) model constructs in women. materials and methods: this study was a ...

The inductive learning problem consists of learning a concept given examples and nonexamples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe ...

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...

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...