Aims and Scope
Machine Intelligence is areas in software engineering and computer science where soft computing and machine intelligence are solutions to computationally hard tasks. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. Machine Intelligence explores virtually all aspects of machine learning, including deep learning and more classical algorithms. Machine Learning is based on Artificial Neural Networks, Support Vector Machines, Classification and Regression Trees, and some more similar methods. Important is that the resulting mathematical models are built by training from example data instead of being constructed analytically. This means that they are example-based or empirical mathematical models instead of analytical ones. This special issue provides a new forum for the dissemination of knowledge on both theoretical and applied research on machine learning with an ultimate aim to bridge the gap between these two disciplines of knowledge. It focuses on technologies that support interaction between the above two bodies of knowledge and fosters unified development in next generation computational models for machine intelligence.
ISSN: TBD (Print)
ISSN: TBD (Online)
Journal of Machine Systems, Intelligence, and Simulations is a journal with free access. Upon posting, all documents are automatically accessible for reading. You will find more detail on our position on Open Access on our copyright agreement.