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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ganzhao Yuan |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China (Ganzhao Yuan) |
| Abstract | The cutting plane algorithm provides fast training for classification SVMs, but it still suffers from the problem of memory restriction, because the algorithm requires to load all the data to the memory. To overcome this bottleneck, we propose and implement a Parallel Cutting Plane algorithm for training Support Vector Machines (PCPSVM) on distributed computers. The Algorithm uses a row-based storage method to reduce memory requirement and finally can parallelize both data loading and computation. Let l denote the number of training instances, d the dimension of each instance, m the number of machines. We divide the data to m parts, and loads only essential data to each machine to perform parallel computation. The memory requirement can be reduced from O(ld) to O(ld/m). We implement our PCPSVM algorithm in the MPICH platform. Experiments show that the algorithm is effective, memory requirement is reduced and great speed-up is achieved when many processors are used. PCPSVM Open Source is available at http://code.google.com/p/pcpsvm/. |
| File Size | 189165 |
| File Format | |
| ISBN | 9781424463473 |
| e-ISBN | 9781424463497 |
| DOI | 10.1109/ICCET.2010.5485293 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-04-16 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cutting Plane Machine learning algorithms Parallel Computing Data engineering Distributed computing Large-Scale Problem Machine Learning SVMs Support vector machines Concurrent computing Computer science Row-based Support vector machine classification Machine learning Large-scale systems Kernel |
| Content Type | Text |
| Resource Type | Article |
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