Now showing items 1-6 of 6
A novel artificial hydrocarbon networks based value function approximation in hierarchical reinforcement learning
(Springer Verlag, 2017)
Reinforcement learning aims to solve the problem of learning optimal or near-optimal decision-making policies for a given domain problem. However, it is known that increasing the dimensionality of the input space (i.e. ...
An indoor predicting climate conditions approach using Internet-of-things and artificial hydrocarbon networks
(Elsevier B.V., 2019)
The prediction and understanding of environmental conditions are of great importance to prevent and analyze changes in environment, supporting meteorological based sectors, such as agriculture or smart cities. In that ...
Stochastic parallel extreme artificial hydrocarbon networks : an implementation for fast and robust supervised machine learning in high-dimensional data
(Elsevier Ltd., 2020-03)
Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other ...
Versatility of artificial hydrocarbon networks for supervised learning
(Springer Verlag, 2019)
Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning ...
Human activity recognition on mobile devices using artificial hydrocarbon networks
(Springer Verlag, 2018)
Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. ...
A comparative analysis of evolutionary learning in artificial hydrocarbon networks
(Springer Science and Business Media Deutschland GmbH, 2020-10-07)
Artificial hydrocarbon networks (AHN) is a supervised learning model that is loosely inspired on the interactions of molecules in organic compounds. This method is able to model data in a hierarchical and robust way. ...