3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models

Combining Simulation with Machine Learning to Build Accident Models

A crucial part of safety-critical systems development is identifying how system behaviours lead to accidents. Doing this for complex systems is hard because of the need to consider the interaction between multiple simultaneous failures and normal system behaviours. Multi-agent simulation provides a way to explore the behaviour that emerges from a model of a system under normal and failure condi...

Combining Unsupervised and Supervised Machine Learning to Build User Models for Intelligent Learning Environments

Traditional approaches to developing user models, especially for computer-based learning environments, are notoriously difficult and time-consuming because they rely heavily on expert-elicited knowledge about the target application and domain. Furthermore, because the necessary expert knowledge is application and domain specific, the entire model development process must be repeated for each ne...

Evaluating Context Selection Strategies to Build Emotive Vector Space Models

In this paper we compare different context selection approaches to improve the creation of Emotive Vector Space Models (VSMs). The system is based on the results of an existing approach that showed the possibility to create and update VSMs by exploiting crowdsourcing and human annotation. Here, we introduce a method to manipulate the contexts of the VSMs under the assumption that the emotive co...

3d Qsar Models

VRI: 3D QSAR at variable resolution

Ž . ABSTRACT: VRI Variable Resolution Invariants is a new approach to quantitative structure]activity relations that makes use of three-dimensional features of molecules at different levels of spatial resolution as well as levels of resolution in atomic properties. These descriptors are independent of any numbering of the atoms of a molecule. They are also independent of rigid translation and r...