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[求助] CFP: SML@IEEE BigData 2013

***************************************************************
                                    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

目標(biāo)和范圍
-----------------------------------------------
大數(shù)據(jù)涵蓋各個(gè)領(lǐng)域，包括互聯(lián)網(wǎng)搜索，社交網(wǎng)絡(luò)，金融、經(jīng)濟(jì)部門(mén)，氣象，基因?qū)W，復(fù)雜物理模擬，以及生物和環(huán)境等多個(gè)領(lǐng)域。大數(shù)據(jù)的4V特點(diǎn)，即數(shù)據(jù)量大(volume)，高速變換(velocity)，種類(lèi)繁多(variety)，精確性低(veracity)，給目前機(jī)器學(xué)習(xí)帶來(lái)了巨大的挑戰(zhàn)。然而，依靠可伸縮的機(jī)器學(xué)習(xí)技術(shù)為大數(shù)據(jù)各個(gè)領(lǐng)域的數(shù)據(jù)建模和分析具有顯著意義。

本次研討會(huì)旨在為專(zhuān)業(yè)人士，研究人員和技術(shù)專(zhuān)家，通過(guò)共同討論，從理論和應(yīng)用方面分享目前最先進(jìn)的可擴(kuò)展的機(jī)器學(xué)習(xí)技術(shù)。

組織者
-----------------------------------------------
* 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

特邀發(fā)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頁(yè))、長(zhǎng)文(6-8頁(yè))。
IEEE 論文格式: 詳見(jiàn)Submission Information in https://sites.google.com/site/bigdatasml/

在線提交系統(tǒng)：https://wi-lab.com/cyberchair/2013/bigdata13/cbc_index.php
請(qǐng)選擇"Workshop/Scalable Machine Learning: Theory and Algorithms"。

論文IEEE索引。每篇文章需一位作者注冊(cè)。

[ Last edited by yhq2k on 2013-7-4 at 13:09 ]

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