This paper introduces CLEO, a novel preference elicitation algorithm capable of recommending complex configurable objects characterized by both discrete and continuous attributes constraints defined over them. While existing techniques focus on searching for the best instance in database candidates, CLEO takes constructive approach to recommendation through interactive optimization space feasib...

The existence or not of pathologies in the context Lagrangian theory is studied with aid Machine Learning algorithms. Using an example framework classical mechanics, we make a proof concept, that construction new physical theories using machine learning possible. Specifically, utilize fully-connected, feed-forward neural network architecture, aiming to discriminate between ``healthy'' and ``non...

Background: Because of approaches to learning in every place and at any time, ubiquitous learning with knowledge of the context and framework, and due to the development of wireless technologies and sensors, the learning process has changed. Mobile learning and ubiquitous learning as models of e-learning that refer to the acquisition of knowledge, attitudes and skills through wireless technolog...

Can statistical machine learning theories and algorithms help explain human learning? Broadly speaking, machine learning studies the fundamental laws that govern all learning processes, including both artificial systems (e.g., computers) and natural systems (e.g., humans). It has long been understood that theories and algorithms from machine learning are relevant to understanding aspects of hum...

We propose the use of the explanations provided by a lazy learning method to build a domain theory. Explanations are understood here as generalizations and as a such we interpret them as domain rules in the same sense that eager learning methods do. Differently than domain theories generated by eager learning methods, theories generated from explanations are partial. In this paper the utility o...