Meta-Learning: Concept Analysis

A Meta-learning Method for Concept Drift

The knowledge hidden in evolving data may change with time, this issue is known as concept drift. It often causes a learning system to decrease its prediction accuracy. Most existing techniques apply ensemble methods to improve learning performance on concept drift. In this paper, we propose a novel meta learning approach for this issue and develop a method: Multi-Step Learning (MSL). In our me...

Deep Meta-Learning: Learning to Learn in the Concept Space

Few-shot learning remains challenging for metalearning that learns a learning algorithm (metalearner) from many related tasks. In this work, we argue that this is due to the lack of a good representation for meta-learning, and propose deep metalearning to integrate the representation power of deep learning into meta-learning. The framework is composed of three modules, a concept generator, a me...

Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris’ distributional hypothesis and model the context of a certain term as a vector representing syn...

Learning Thresholds in Formal Concept Analysis

This paper views Formal Concept Analysis (FCA) from an educational perspective. Novice users of FCA who are not mathematicians might find diagrams of concept lattices counter-intuitive and challenging to read. According to educational theory, learning thresholds are concepts that are difficult to learn and easy to be misunderstood. Experts of a domain are often not aware of such learning thresh...

Machine Learning and Formal Concept Analysis