会议信息

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

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截稿日期:
2017-05-31
通知日期:
2017-07-01
会议日期:
2017-09-14
会议地点:
Volterra, Tuscany, Italy
届数:
3
浏览: 14621   关注: 1   参加: 0

会伴指数 (CP-I)

40.2 / 100
全站第 5,020 名 / 共 5,693 个会议 · 前 89%

数据挖掘与数据库 第 302 / 337 人工智能与机器学习 第 642 / 742

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
37
社区关注 (10%)
15
资料公开度 (15%)
25

用到的输入: 有据可查的届次：3 · 在会伴关注它的研究者：1 人 · 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-05

征稿

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
由 Dou Sun 最后更新于

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