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CoDS 2016 : Conference on Data Science
Link: http://ikdd.acm.org/Site/CoDS2016/cods2016_cfp.html
Categories ML data science
Details: http://ikdd.acm.org/Site/CoDS2016/cods2016_cfp.html
The topics of interest for invited submissions include, but are not limited to:
Models and Algorithms: big data analytics, parallel and distributed machine learning, statistical learning theory, classification and regression methods, semi-supervised learning, unsupervised learning and clustering, kernel methods, online learning, reinforcement learning, graph mining, rule and pattern mining, relational and structured learning, matrix and tensor methods, time series analysis, mining temporal and spatial data, dimensionality reduction and manifold learning, combinatorial and continuous optimization, probabilistic graphical models, Bayesian methods, neural networks and deep learning.
Applications: text analytics and natural language processing, information retrieval, social network analysis, web and social media analysis, recommender systems, online advertising, bioinformatics, computational neuroscience, systems biology, multimedia processing, crowdsourcing, education, robotics, technology for emerging markets.
Industrial and Government Case studies: Descriptions of deployments of data science solutions in industry and government that address real-world challenges and highlight new and important research directions for data science. Domains of interest include but are not restricted to e-commerce, retail, online advertising, telecommunications, banking and finance, consumer products, media and entertainment, healthcare, education, manufacturing, natural resources, public safety, urban planning.
The submissions should not exceed 10 pages and should be formatted in the standard ACM style available at http://www.acm.org/sigs/pubs/proceed/template.html. The submissions should be made to https://www.easychair.org/conferences/?conf=cods2016.
Dual Submission Policy
CoDS does not allow dual submission to other conferences/journals, violating papers will be rejected immediately without review.
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