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A Distributed Machine Learning Framework Tansu Alpcan and Christian Bauckhage
 

Summary: A Distributed Machine Learning Framework
Tansu Alpcan and Christian Bauckhage
Abstract-- A distributed online learning framework for sup-
port vector machines (SVMs) is presented and analyzed. First,
the generic binary classification problem is decomposed into
multiple relaxed subproblems. Then, each of them is solved
iteratively through parallel update algorithms with minimal
communication overhead. This computation can be performed
by individual processing units, such as separate computers or
processor cores, in parallel and possibly having access to only
a subset of the data.
Convergence properties of continuous- and discrete-time
variants of the proposed parallel update schemes are studied.
A sufficient condition is derived under which synchronous and
asynchronous gradient algorithms converge to the approximate
solution. Subsequently, a class of stochastic update algorithms,
which may arise due to distortions in the information flow
between units, is shown to be globally stable under similar
sufficient conditions. Active set methods are utilized to decrease
communication and computational overhead. A numerical ex-

  

Source: Alpcan, Tansu - Deutsche Telekom Laboratories & Technische Universitšt Berlin

 

Collections: Engineering