<p>If interested in online learning with concept drift then here is some previous work</p>

<ol>
<li><p>Learning under Concept Drift: an Overview
<a href="https://arxiv.org/pdf/1010.4784.pdf" rel="nofollow">https://arxiv.org/pdf/1010.4784.pdf</a></p></li>
<li><p>The problem of concept drift: definitions and related work
<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.9085&amp;rep=rep1&amp;type=pdf" rel="nofollow">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.9085&amp;rep=rep1&amp;type=pdf</a></p></li>
<li><p>A Survey on Concept Drift Adaptation
<a href="http://www.win.tue.nl/~mpechen/publications/pubs/Gama_ACMCS_AdaptationCD_accepted.pdf" rel="nofollow">http://www.win.tue.nl/~mpechen/publications/pubs/Gama_ACMCS_AdaptationCD_accepted.pdf</a></p></li>
<li><p>MOA Concept Drift Active Learning Strategies for Streaming Data
<a href="http://videolectures.net/wapa2011_bifet_moa/" rel="nofollow">http://videolectures.net/wapa2011_bifet_moa/</a></p></li>
<li><p>A Stream of Algorithms for Concept Drift
<a href="http://people.cs.georgetown.edu/~maloof/pubs/maloof.heilbronn12.handout.pdf" rel="nofollow">http://people.cs.georgetown.edu/~maloof/pubs/maloof.heilbronn12.handout.pdf</a></p></li>
<li><p>MINING DATA STREAMS WITH CONCEPT DRIFT
<a href="http://www.cs.put.poznan.pl/dbrzezinski/publications/ConceptDrift.pdf" rel="nofollow">http://www.cs.put.poznan.pl/dbrzezinski/publications/ConceptDrift.pdf</a></p></li>
<li><p>Analyzing time series data with stream processing and machine learning
<a href="http://www.ibmbigdatahub.com/blog/analyzing-time-series-data-stream-processing-and-machine-learning" rel="nofollow">http://www.ibmbigdatahub.com/blog/analyzing-time-series-data-stream-processing-and-machine-learning</a></p></li>
</ol>
Does the SVM in sklearn support incremental (online) learning?
<p>I am currently in the process of designing a recommender system for text articles (a binary case of 'interesting' or 'not interesting'). One of my specifications is that it should continuously update to changing trends. </p>

<p>From what I can tell, the best way to do this is to make use of machine learning algorithm that supports incremental/<a href="http://en.wikipedia.org/wiki/Online%5fmachine%5flearning">online learning</a>. </p>

<p>Algorithms like the Perceptron and Winnow support online learning but I am not completely certain about Support Vector Machines. Does the scikit-learn python library support online learning and if so, is a support vector machine one of the algorithms that can make use of it?</p>

<p>I am obviously not completely tied down to using support vector machines, but they are usually the go to algorithm for binary classification due to their all round performance. I would be willing to change to whatever fits best in the end.</p>