Machine Learning Methods with Noisy, Incomplete or Small Datasets

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Machine Learning Methods with Noisy, Incomplete or Small Datasets is written by Jordi Solé-Casals and published by MDPI. It's available with International Standard Book Number or ISBN identification 3036512888 (ISBN 10) and 9783036512884 (ISBN 13).

Over the past years, businesses have had to tackle the issues caused by numerous forces from political, technological and societal environment. The changes in the global market and increasing uncertainty require us to focus on disruptive innovations and to investigate this phenomenon from different perspectives. The benefits of innovations are related to lower costs, improved efficiency, reduced risk, and better response to the customers’ needs due to new products, services or processes. On the other hand, new business models expose various risks, such as cyber risks, operational risks, regulatory risks, and others. Therefore, we believe that the entrepreneurial behavior and global mindset of decision-makers significantly contribute to the development of innovations, which benefit by closing the prevailing gap between developed and developing countries. Thus, this Special Issue contributes to closing the research gap in the literature by providing a platform for a scientific debate on innovation, internationalization and entrepreneurship, which would facilitate improving the resilience of businesses to future disruptions. Order Your Print Copy