Machine learning and concept formation

The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning

Machine learning is usually defined in behaviourist terms, where external validation is the primary mechanism of learning. In this paper, I argue for a more holistic interpretation in which finding more probable, efficient and abstract representations is as central to learning as performance. In other words, machine learning should be extended with strategies to reason over its own learning pro...

Concept Reliability in Machine Learning

Much machine learning research addresses inductive learning — learning relationships from a set of examples (Michalski (1986) provides an excellent introduction). For instance, some programs have been used to learn medical diagnostic rules from a database of patients whose diagnoses are known. These programs examine a number of attributes (e.g. age, temperature, and pulse rate) for a set of exa...

Machine Learning and Formal Concept Analysis

Graduiertenkolleg " Informatik Und Technik\ Concept Formation by Combining Neural Networks and Machine Learning

Complex feature patterns occur in many technical areas. They consist of received sensor signals or measured values. The knowledge about the interpretation of such patterns can not often be formalized with conventional knowledge acquisition methods. Furthermore, direct learning of concepts from received signal values is impossible with machine learning methods, because of the necessity of a logi...

2D1431 Machine Learning Lab 1: Concept Learning & Decision Trees

You have to prepare the solutions to the lab assignments prior to the scheduled labs, which are mainly for examination. In order to pass the lab you present your program and answers to the question to the assistent. Labs can be presented in groups of two, however both students need to fully understand the entire solution and answers. It is also assumed that you complete the assignment on your o...