Now showing items 1-5 of 5
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. ...
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 ...
Interpretability of artificial hydrocarbon networks for breast cancer classification
(Institute of Electrical and Electronics Engineers Inc., 2017)
In machine learning, interpretability refers to understand the underlying behavior of the prediction of a model in order to identify diagnosis criteria and/or new rules from its output. Interpretability contributes to ...
Predicting climate conditions using internet-of- things and artificial hydrocarbon networks
(IMEKO-International Measurement Federation Secretariat, 2018-02)
The prediction and understanding of environmental conditions is of great importance to prevent and analyze changes in environment, supporting meteorological based sectors, such as agriculture. In that sense, this paper ...