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      Introduction to MAchine Learning & Knowledge Extraction (MAKE)

      Machine Learning and Knowledge Extraction
      MDPI AG

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          Abstract

          The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning & Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community.

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          Most cited references60

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          Consensus and Cooperation in Networked Multi-Agent Systems

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            Deep Learning in Neural Networks: An Overview

            (2014)
            In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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              Taking the Human Out of the Loop: A Review of Bayesian Optimization

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                Author and article information

                Journal
                Machine Learning and Knowledge Extraction
                MAKE
                MDPI AG
                2504-4990
                December 2018
                July 03 2017
                : 1
                : 1
                : 1-20
                Article
                10.3390/make1010001
                f9b82e64-d373-40f9-8d34-47d7580bddae
                © 2017

                https://creativecommons.org/licenses/by/4.0/

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