Suboptimal Solution Path Algorithm for Support Vector Machine

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Suboptimal Solution Path Algorithm for Support Vector Machine
We consider a suboptimal solution path algorithm for the Support Vector Machine. The solution path algorithm is an effective tool for solving a sequence of a parametrized optimization problems in machine learning. The path of the solutions provided by this algorithm are very accurate and they satisfy the optimality conditions more strictly than other SVM optimization algorithms. In many machine learning application, however, this strict optimality is often unnecessary, and it adversely affects the computational efficiency. Our algorithm can generate the path of suboptimal solutions within an arbitrary user-specified tolerance level. It allows us to control the trade-off between the accuracy of the solution and the computational cost. Moreover, We also show that our suboptimal solutions can be interpreted as the solution of a perturbed optimization problem from the original one. We provide some theoretical analyses of our algorithm based on this novel interpretation. The experiment...
Masayuki Karasuyama, Ichiro Takeuchi
Added 19 Aug 2011
Updated 19 Aug 2011
Type Journal
Year 2011
Where CORR
Authors Masayuki Karasuyama, Ichiro Takeuchi
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