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MOD 2017: International Conference on Machine learning, Optimization, and big Data

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투고 마감일:
2017-05-31
통보일:
2017-07-01
개최일:
2017-09-14
개최지:
Volterra, Tuscany, Italy
개최 횟수:
3
조회: 13831   팔로우: 1   참가: 0

논문 모집

MOD 2017 (International Conference on Machine learning, Optimization, and big Data) is an academic conference held in Volterra, Tuscany, Italy on 2017-09-14. The paper submission deadline is 2017-05-31. Acceptance notifications are sent on 2017-07-01.

The International Conference on Machine learning, Optimization, and big Data (MOD) has established itself as a premier interdisciplinary conference in machine learning, computational optimization, knowledge discovery and data science. It provides an international forum for presentation of original multidisciplinary research results, as well as exchange and dissemination of innovative and practical development experiences. MOD 2017 will be held in Volterra (Pisa) – Tuscany, Italy, from September 14 to 17, 2017. The conference will consist of four days of conference sessions. We invite submissions of papers on all topics related to Machine learning, Optimization, Knowledge Discovery and Data Science including real-world applications for the Conference Proceedings by Springer – Lecture Notes in Computer Science (LNCS). MOD uses the single session formula of 30 minutes presentations for fruitful exchanges between authors and participants. Topics of Interest The last five-year period has seen a impressive revolution in the theory and application of machine learning and big data. Topics of interest include, but are not limited to: Foundations, algorithms, models and theory of data science, including big data mining. Machine learning and statistical methods for big data. Machine Learning algorithms and models. Neural Networks and Learning Systems. Convolutional neural networks. Unsupervised, semi-supervised, and supervised Learning. Knowledge Discovery. Learning Representations. Representation learning for planning and reinforcement learning. Metric learning and kernel learning. Sparse coding and dimensionality expansion. Hierarchical models. Learning representations of outputs or states. Multi-objective optimization. Optimization and Game Theory. Surrogate-assisted Optimization. Derivative-free Optimization. Big data Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data. Big Data mining systems and platforms, and their efficiency, scalability, security and privacy. Computational optimization. Optimization for representation learning. Optimization under Uncertainty Optimization algorithms for Real World Applications. Optimization for Big Data. Optimization and Machine Learning. Implementation issues, parallelization, software platforms, hardware Big Data mining for modeling, visualization, personalization, and recommendation. Big Data mining for cyber-physical systems and complex, time-evolving networks. Applications in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, medicine and other domains. We particularly encourage submissions in emerging topics of high importance such as data quality, advanced deep learning, time-evolving networks, large multi-objective optimization, quantum discrete optimization, learning representations, big data mining and analytics, cyber-physical systems, heterogeneous data integration and mining, autonomous decision and adaptive control. https://easychair.org/conferences/?conf=mod2017
최종 수정: Dou Sun ()

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관련 저널

CCF정식 명칭영향력 지수출판사ISSN
BMachine Learning2.9Springer0885-6125
Computational Optimization and Applications2.0Springer0926-6003
Journal of Big Data6.4Springer2196-1115
Big Data Research4.2Elsevier2214-5796
Discrete Optimization1.6Elsevier1572-5286
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems6.1Elsevier0167-739X

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