sábado, 18 de febrero de 2017

Call for Papers 2017 Conference for Machine Learning and Knowledge Extraction Calabria SEP 2017


Hoy traemos a este espacio a CD-MAKE
Cross Domain Conference for Machine Learning and Knowledge Extraction
co-located with ARES 2017, Reggio Calabria, Italy August 29 - September 1, 2017
IFIP – the International Federation for Information Processing is the leading multi-national, non-governmental, apolitical organization in Information & Communications Technologies and Computer Sciences, is recognized by the United Nations and was established in the year 1960 under the auspices of the UNESCO as an outcome of the first World Computer Congress held in Paris in 1959.
IFIP is incorporated in Austria by decree of the Austrian Foreign Ministry (20th September 1996, GZ 1055.170/120-I.2/96) granting IFIP the legal status of a non-governmental international organisation under the Austrian Law on the Granting of Privileges to Non-Governmental International Organisations (Federal Law Gazette 1992/174).
IFIP brings together more than 3500 scientists without boundaries form both academia and industry, organized in more than 100 Working Groups (WG) and 13 Technical Committees (TC).
The International Cross Domain Conference for Machine Learning & Knowledge Extraction CD-MAKE
is a joint effort of IFIP TC 5, IFIP WG 8.4, IFIP WG 8.9 and IFIP WG 12.9 and is held in conjunction with the International Conference on Availability, Reliability and Security (ARES).
CD stands for Cross-Domain and means the integration and appraisal of different fields and application domains (e.g. Health, Industry 4.0, etc.) to provide an atmosphere to foster different perspectives and opinions. The conference is dedicated to offer an international platform for novel ideas and a fresh look on the methodologies to put crazy ideas into Business for the benefit of the human. Serendipity is a desired effect, and shall cross-fertilize methodologies and transfer of algorithmic developments. 
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, etc. The 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. Whether in social networks, recommender systems, health or Industry 4.0 applications, the increasingly complex data sets require efficient, useful and useable solutions 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.
Consequently, successful Machine Learning & Knowledge extraction needs a concerted international effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts from 7 fields (image below):
❶ Data science (i.e. data fusion, data preprocessing, data mapping, knowledge representation),
❷ Machine learning,
❸ Graphical models/network science,
❹ Topological Data Analytics,
❺ Time/entropy,
❻ Data visualization, and last but not least – and with increasing imporance in our ML-society:
❼ Privacy, data protection, safety and security.
The goal of the CD-MAKE conference is to bring together researchers from these seven areas in an cross-disciplinary manner, to stimulate fresh ideas and to encourage multi-disciplinary problem solving.

Successful machine learning needs a concerted effort supporting the whole Knowledge Extraction/Discovery Pipeline!

Call for Papers 2017

—- Call for Papers —-
The International Cross Domain Conference for Machine Learning & Knowledge Extraction CD-MAKE is a joint effort of IFIP TC 5, IFIP WG 8.4, IFIP WG 8.9 and IFIP WG 12.9 and is held in conjunction with the International Conference on Availability, Reliability and Security (ARES) and takes place
in Reggio (Italy) from August, 29 to September, 1, 2017.
CD stands for Cross-Domain and means the integration and appraisal of different fields and application domains (e.g. Health, Industry 4.0, etc.) to provide an atmosphere to foster different perspectives and opinions. The conference is dedicated to offer an international platform for novel ideas and a fresh look on the methodologies to put crazy ideas into Business for the benefit of the human.
MAKE stands for MAchine Learning & Knowledge Extraction.
Papers are sought from the following seven topical areas (see image below). Papers which deal with fundamental questions and theoretical aspects in machine learning are very welcome.
❶ Data science (data fusion, preprocessing, data mapping, knowledge representation),
❷ Machine learning (both automatic ML and interactive ML with the human-in-the-loop),
❸ Graphs/network science (i.e. graph-based data mining),
❹ Topological data analysis (i.e. topological data mining),
❺ Time/entropy (i.e. entropy-based data mining),
❻ Data visualization (i.e. visual analytics), and last but not least
❼ Privacy, data protection, safety and security (i.e. privacy aware machine learning).
Proposal for Workshops, Speical Session, Tutorials: March, 6, 2017
Submission Deadline: April, 01, 2017
Accepted Papers will be published in a Springer Volume of Lecture Notes in Artificial Intelligence (LNAI). Outstanding contributions will be invited to special issues of journals (tba).
The CD-MAKE conference system (EasyChair) will soon be available here: https://easychair.org/conferences/?conf=cdmake2017 – select CD-MAKE

Successful machine learning needs a concerted effort supporting the whole Knowledge Extraction/Discovery Pipeline!
(leer más...) Fuente: [ CD Make2017]

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