Open AccessProceedings Article10.1109/IJCNN.2002.1007516

SSVM: a simple SVM algorithm

- 07 Aug 2002

- Vol. 3, pp 2393-2398

212

TL;DR: A fast iterative algorithm for identifying the support vectors of a given set of points using a greedy approach to pick points for inclusion in the candidate set, which is extremely competitive as compared to other conventional iterative algorithms like SMO and the NPA.

Abstract: We present a fast iterative algorithm for identifying the support vectors of a given set of points. Our algorithm works by maintaining a candidate support vector set. It uses a greedy approach to pick points for inclusion in the candidate set. When the addition of a point to the candidate set is blocked because of other points already present in the set, we use a backtracking approach to prune away such points. To speed up convergence we initialize our algorithm with the nearest pair of points from opposite classes. We then use an optimization based approach to increase or prune the candidate support vector set. The algorithm makes repeated passes over the data to satisfy the KKT constraints. The memory requirements of our algorithm scale as O(|SI|/sup 2/) in the average case, where |S| is the size of the support vector set. We show that the algorithm is extremely competitive as compared to other conventional iterative algorithms like SMO and the NPA. We present results on a variety of real life datasets to validate our claims.

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Most frequently asked questions

1. What are the contributions in this paper?

The authors present a fast iterative algorithm for identifying the Support Vectors of a given set of points.. The authors show that the algorithm is extremely competitive as compared to other conventional iterative algorithms like SMO and the NPA.. The authors present results on a variety of real life datasets to validate their claims.. The authors then use an optimization based approach to increment or prune the candidate Support Vector set.

2. How many times does the algorithm need to backtrack?

It can be seen that the Simple SVM algorithm needs to backtrack only around 20% of the times and hence the penalty incurred due to a greedy approach is not very significant.

3. How many times did the algorithm backtrack?

The authors show that the algorithm significantly outper-1191 898 2.88 898 14.1 1176 776 2.82 776 26.2 1188 735 3.18 734 33.70-7803-7278-6/02/~10.00 02002 IEEE500 100023971200 687 3.08 687 56.9 1224 677 3.20 677 66.0forms other iterative algorithms like the NPA in terms of the number of kernel computations.

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References

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The Nature of Statistical Learning Theory

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TL;DR: SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.

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Fast training of support vector machines using sequential minimal optimization

John Platt

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Incremental and Decremental Support Vector Machine Learning

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TL;DR: An on-line recursive algorithm for training support vector machines, one vector at a time, is presented and interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of the data.

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1.4K

SSVM: a simple SVM algorithm

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Most frequently asked questions

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Citations

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Machine-Learning-Assisted Security and Privacy Provisioning for Edge Computing: A Survey

References

The Nature of Statistical Learning Theory

UCI Repository of machine learning databases

Fast training of support vector machines using sequential minimal optimization, advances in kernel methods

Fast training of support vector machines using sequential minimal optimization

Incremental and Decremental Support Vector Machine Learning

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