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 ecosystem services with special attention on sustainability issues, (3) To integrate the fragmented knowledge on ecosystem services, 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 ecosystem services assessment and valuation and support its mainstreaming into economic and land-use management policies.

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.

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