On Selected Methods of Machine Learning for Environment
Kratka vsebina
Machine learning methods are increasingly used to analyze complex systems in both natural and social environments. In this paper, we present an overview of selected types of machine learning (reinforcement learning, supervised learning, and unsupervised learning), and discuss their applicability to environmental monitoring and simulated social environments. Supervised learning methods are shown to support prediction and classification tasks based on labeled environmental and social data, while unsupervised learning enables the discovery of hidden structures and patterns without prior labeling. We present reinforcement learning as a framework for adaptive decision-making, allowing agents to learn optimal behavior through interaction with dynamic environments. We provide illustrative examples, including weight updates in neural networks and Q-learning updates, to clarify the learning mechanisms. The presented approaches demonstrate the practical relevance of machine learning for modeling, analyzing, and understanding socio-environmental systems.
Prenosi
Strani
699-712
Izdano
17 marec 2026
Kategorije
- Ekonomija
- Logistika
- Matematika
- Podjetništvo
- Poslovne vede
- Računalništvo in informatika
- Sociologija
- Strojništvo
- Turizem
- Upravne in organizacijaske vede
- Varnostne vede
- Varstvo okolja
- Vzgoja in izobraževanje
- Zdravstvene vede
- 2026
- Zbornik konference
- Odprti dostop
- Univerza v Mariboru, Fakulteta za organizacijske vede
- Slovenski jezik
- Angleški jezik
- Večjezično
Avtorske pravice (c) 2026 Univerza v Mariboru, Univerzitetna založba
