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CFP: SML@IEEE BigData 2013
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*************************************************************** CALL FOR PAPERS *************************************************************** SML@IEEE BigData 2013: International Workshop on Scalable Machine Learning: Theory and Applications https://sites.google.com/site/bigdatasml/home Co-located with IEEE BigData 2013, October 6, 2013, Santa Clara, CA, USA 目标和范围 ----------------------------------------------- 大数据涵盖各个领域，包括互联网搜索，社交网络，金融、经济部门，气象，基因学，复杂物理模拟，以及生物和环境等多个领域。大数据的4V特点，即数据量大(volume)，高速变换(velocity)，种类繁多(variety)，精确性低(veracity)，给目前机器学习带来了巨大的挑战。然而，依靠可伸缩的机器学习技术为大数据各个领域的数据建模和分析具有显著意义。 本次研讨会旨在为专业人士，研究人员和技术专家，通过共同讨论，从理论和应用方面分享目前最先进的可扩展的机器学习技术。 组织者 ----------------------------------------------- * Irwin King, The Chinese University of Hong Kong * Michael R. Lyu, The Chinese University of Hong Kong * Michael Mahoney, Stanford University * Zenglin Xu, Purdue University * Haiqin Yang, The Chinese University of Hong Kong 特邀发言人 ----------------------------------------------- * Mikhail Bilenko, Microsoft research * Carlos Guestrin, University of Washington * Alek Kolcz, Twitter * Alex Smola, Carnegie Mellon University TOPICS OF INTEREST ----------------------------------------------- Topics of interest include, but not limited to: * Distributed machine learning architectures - Data separation and integration techniques - Machine learning algorithms for GPUs - Machine learning algorithms for clouds - Machine learning algorithms for clusters * Theory and algorithms of data reduction techniques for Big Data - Online/incremental learning algorithms - Random projection - Hashing techniques - Data sampling algorithms * Theory and algorithms of large-scale matrix approximation - Bound analysis of matrix approximation algorithms - Parallel matrix factorization - Parallel multiway array factorization - Online dictionary learning - Distributed topic modeling algorithms * Heterogeneous learning on Big multi-modality Data - Multiview learning - Multitask learning - Transfer learning - Semi-supervised learning - Active learning * Temporal analysis and spatial analysis in Big Data - Real time analysis for data stream - Trend prediction in financial data - Topic detection in instant message systems - Real time modeling of events in dynamic networks - Spacial modeling on maps * Scalable Machine Learning in large graphs - Communities discovery and analysis in social networks - Link prediction in networks - Anomaly detection in social networks - Authority identification and influence measurement in social networks - Fusion of information from multiple blogs, rating systems, and social networks - Integration of text, videos, images, sounds in social media - Recommender systems * Novel applications of scalable machine learning in - Healthcare - Cybersecurity - Mobile computing such as location-based service, mobile networks, etc. - Smart cities - Astronomy - Biological data analysis 重要日期 ----------------------------------------------- * August 2, 2013: Due date for workshop papers submission * August 30, 2013: Notification of paper decision to authors * September 25, 2013: Camera-ready of accepted papers * October 6 2013: Workshop 投稿信息 ----------------------------------------------- 短文(2-4页)、长文(6-8页)。 IEEE 论文格式: 详见Submission Information in https://sites.google.com/site/bigdatasml/ 在线提交系统：https://wi-lab.com/cyberchair/2013/bigdata13/cbc_index.php 请选择"Workshop/Scalable Machine Learning: Theory and Algorithms"。 论文IEEE索引。每篇文章需一位作者注册。 [ Last edited by yhq2k on 2013-7-4 at 13:09 ] |
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yzyhb
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