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Machine Learning Knowledge Extraction MAKE it short

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Published on May 13, 2017

MAKE stands for MAchine Learning & Knowledge Extraction.

Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving (Google car), etc. The grand challenge is in decision making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and statistical learning. The inverse probability allows to infer unknowns, learn from data and make predictions to support decision making. Whether in social networks, recommender systems, smart health or smart factory applications, the increasingly complex data sets require efficient, useful and useable intelligence for knowledge discovery and knowledge extraction.

A synergistic combination of methodologies and approaches of two domains offer ideal conditions towards unraveling these challenges and to foster new, efficient and user-friendly machine learning algorithms and knowledge extraction tools: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), aiming at augmenting human intelligence with computational intelligence and vice versa.

Successful Machine Learning & Knowledge extraction needs a concerted international effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts from 7 fields: in short: 1-data, 2-learning, 3-graphs, 4-topology, 5-entropy, 6-visualization, and 7-privacy; see
http://hci-kdd.org/about-the-holzinge...
https://cd-make.net/about/
http://www.mdpi.com/journal/make/about

Andreas Holzinger, 14.05.2017

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