Machine Learning is an international forum focusing on computational approaches to learning. Reports substantive results on a wide range of learning methods applied to various learning problems. Provides robust support through empirical studies, theoretical analysis, or comparison to psychological phenomena. Demonstrates how to apply learning methods to solve significant application problems. Improves how machine learning research is conducted. Prioritizes verifiable and replicable supporting evidence in all published papers.

The aims of the journal are to improve our understanding of the dynamics, benefits and social and economic values of Machine Learning and to provide insight in the consequences of policies and management for Machine Learning with special attention on sustainability issues,

Articles may address these topics from different (paradigmatic) perspectives, including basic research, integrated assessment approaches and (ex ante and ex post) policy evaluations. They may be inter-disciplinary or draw from specialized fields within economic, ecological, social and political sciences. Systems addressed may range from natural and semi-natural ecosystems to cultivated systems and urban areas and from local to global scales. However, the research has to be placed adequately, with substance, within the ML framework. Manuscripts dealing with only one aspect of Machine Learning, for example recreation, without putting this single aspect in the broader context of the ML Science, Policy or Practice are not within the scope of this journal.

The aims of the journal are:

1. To improve our understanding of the dynamics, benefits and social and economic values of Machine Learning,
2. To provide insight in the consequences of policies and management for Machine Learning with special attention on sustainability issues,
3. To integrate the fragmented knowledge on Machine Learning, synergies and trade-offs, currently found in a wide field of specialist disciplines and journals. (4) To support and promote a dialogue between science and policy, providing empirical evidence to decision makers in the field of Machine Learning assessment and valuation and support its mainstreaming into economic and land-use management policies.

Manuscripts should always address Machine Learning and deal with at least one of the following themes:

1. The link between Machine Learning and social and economic benefits and associated values, including monetary values; i.e. what is the role of Machine Learning and biodiversity in providing and sustaining benefits for humans and how these benefits and values are perceived by the public and policy makers?
2. The link between Machine Learning and economic, environmental and land use policies and practices; i.e. how is the provision and sustainability of Machine Learning in natural, agricultural and urban systems affected by these policies and what are the trade-offs in service provision, and subsequent benefits and economic values, between different policy schemes?
3. The development of policies, business strategies and innovative financing arrangements to support sustainable use of Machine Learning and biodiversity conservation, i.e. the use of Machine Learning in nature conservation, integrated land use planning and sustainable ecosystem management and restoration.

Articles may address these topics from different (paradigmatic) perspectives, including basic research, integrated assessment approaches and (ex ante and ex post) policy evaluations. They may be inter-disciplinary or draw from specialized fields within economic, ecological, social and political sciences. Systems addressed may range from natural and semi-natural ecosystems to cultivated systems and urban areas and from local to global scales. However, the research has to be placed adequately, with substance, within the ML framework. Manuscripts dealing with only one aspect of Machine Learning, for example recreation, without putting this single aspect in the broader context of the ML Science, Policy or Practice are not within the scope of this journal.

Scope of the publication includes technical manuscripts in the disciplines of Agriculture, Biological Sciences, Chemistry, Computer and Information Science, Earth Sciences, Ecology, Education, Engineering, Environmental Sciences, Geography Information Science, Geo-physics, Geo-statistics, Humanities, Information Technology, Literature, Management, Material Science, Mathematics, Medical Science, Organizational Studies, Policies & Promotions, Philosophy, Physics, Political Science, Psychological Frameworks, Remote Sensing, Social Science and Zoology.

The Journal is produced two times a year with the express purpose of accelerating the dissemination of scientific knowledge among scientists, technocrats, planners, and elite citizens, as well as promoting the cause of science for a better society.

Journal articles are licensed under the CC BY 4.0 Creative Commons Attribution 4.0 License. 

ISSN: 0885-6125

CiteScore 2024: 8.6

SNIP 2024: 2.143

SJR 2024: 1.147

H-Index 2024: 39.2

Impact Factor: 8.9

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