<div dir="ltr"><div class="gmail_default" style="color:#20124d"><br></div><div class="gmail_quote"><br><div dir="ltr"><div style="color:rgb(32,18,77)"><div class="gmail_quote"><div dir="ltr"><div><div><font color="#20124d"><b>Call for papers</b></font></div><div><font color="#20124d"><b><br></b></font></div><div><font color="#20124d"><b><div>You are invited you to submit an article to the 2nd International Workshop on Machine Learning and Data Mining for Sensor Networks (MLDM-SN), June 2-5, 2015 in London, UK</div><div><br></div><div>The website of MLDM-SN 2015 is available at <a href="http://cs-conferences.acadiau.ca/MLDM-SN15/" target="_blank">http://cs-conferences.acadiau.ca/MLDM-SN15/</a></div></b></font></div><div><font color="#20124d"><b><br></b></font></div><div><br></div><div><font color="#20124d"> </font></div><table align="center" border="0" cellpadding="2" cellspacing="2" width="780" style="font-family:'Times New Roman'"><tbody><tr><td align="center" width="686"><div align="center"><span style="font-family:Arial,Helvetica,sans-serif;font-size:10pt;text-align:justify"><font size="6">The 2nd International Workshop on Machine Learning and Data Mining for Sensor Networks (MLDM-SN)<br><br></font>MLDM-SN 2015 will be held in conjunction with the 6th International Conference on Ambient Systems, Networks and Technologies<a href="http://cs-conferences.acadiau.ca/ant-15/" style="text-decoration:none" target="_blank">(ANT 2015</a>)<br><br>London, UK </span><br>June 2-5, 2015<span style="font-family:Arial,Helvetica,sans-serif;font-size:10pt;text-align:justify"></span><br></div></td></tr><tr><td valign="top"><p> </p><p style="font-family:Arial,Helvetica,sans-serif;font-size:10pt;text-align:justify">This workshop aims to bring together researchers and practitioners working on different aspects of machine learning, data mining and sensor networks technologies in an effort to highlight the state-of-the-art and discuss the challenges and opportunities to explore new research directions.</p><p style="font-family:Arial,Helvetica,sans-serif;font-size:10pt;text-align:justify">The main topics to be addressed include (but not limited to):</p><ul style="font-family:Arial,Helvetica,sans-serif;font-size:10pt;text-align:justify"><li>Software agents approaches.</li><li>Data mining processes including data selection, sampling, cleaning, reduction, transformation, integration and aggregation, as well as model development, validation and deployment.</li><li>Data mining approaches to overcome sensor limitations such as available energy for transmission, computational power, memory, and communications bandwidth.</li><li>Distributed Bayesian learning (belief networks, decision networks)</li><li>Distributed clustering methods (distributed k-Means, dynamic neural networks)</li><li>Distributed machine learning (neural networks, support vector machines, decisions trees and rules, genetic algorithms) in sensor networks</li><li>Distributed Principal Component Analysis (PCA) and Independent Component Analysis (ICA)</li><li>Distributed statistical regression methods in sensor networks.</li><li>Efficient, scalable and distributed algorithms for large-scale DDM tasks such as classification, prediction, link analysis, time series analysis, clustering, and anomaly detection.</li><li>Incremental, exploratory and interactive mining.</li><li>Mining of data streams.</li><li>Power consumption characteristics of distributed data mining algorithms and developing data mining algorithms to minimize power consumption.</li><li>Privacy sensitive data mining.</li><li>Applications of data mining for senor networks in business, science, engineering, medicine, and other disciplines with particular attention to lessons learned.</li><li>Theoretical foundations in data mining and sensor network; extensions of computational learning theory to sensor networks.</li><li>Visual data mining.<span class="HOEnZb"><font color="#888888"><br></font></span></li></ul></td></tr></tbody></table></div><span class="HOEnZb"><font color="#888888"><div class="gmail_extra"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div style="font-size:13px;line-height:11.25pt"><span style="color:rgb(162,162,162);font-family:Arial,sans-serif;font-size:10pt"></span></div><p></p></div></div></div></div></div></div></font></span></div></div><span class="HOEnZb"><font color="#888888"><br><br clear="all"><div><br></div>-- <br><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div style="color:rgb(34,34,34)"><div style="font-family:arial;color:rgb(32,18,77);display:inline"></div><div style="font-family:arial;color:rgb(32,18,77);display:inline"></div><div style="color:rgb(32,18,77);display:inline"></div><img src="cid:_1_090AE1F0090ADCBC005519A585257DA4"><br></div><div style="color:rgb(34,34,34)"><div style="font-size:13px;line-height:11.25pt"><span style="color:rgb(162,162,162);font-family:Arial,sans-serif;font-size:10pt"></span></div></div></div></div></div></div></div></div></font></span></div><div><br></div><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div style="text-align:left"><p></p></div></div></div></div></div></div></div></div></div></div></div></div>
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