Name
Title | ||
|---|---|---|
A Kernel Support Vector Machine Trained Using Approximate Global and Exhaustive Local Sampling. |
Abstract | ||
|---|---|---|
AGEL-SVM is an extension to a kernel Support Vector Machine (SVM) and is designed for distributed computing using Approximate Global Exhaustive Local sampling (AGEL)-SVM. The dual form of SVM is typically solved using sequential minimal optimization (SMO) which iterates very fast if the full kernel matrix can fit in a computeru0027s memory. AGEL-SVM aims to partition the feature space into sub problems such that the kernel matrix per problem can fit in memory by approximating the data outside each partition. AGEL-SVM has similar Cohenu0027s Kappa and accuracy metrics as the underlying SMO implementation. AGEL-SVMu0027s training times greatly decreased when running on a 128 worker MATLAB pool on Amazonu0027s EC2. Predictor evaluation times are also faster due to a reduction in support vectors per partition. |
Year | Venue | Field |
|---|---|---|
2017 | BDCAT | Kernel (linear algebra),Data mining,Feature vector,MATLAB,Computer science,Support vector machine,Algorithm,Sampling (statistics),Sequential minimal optimization,Partition (number theory),Iterated function |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Bryant | 1 | 0 | 0.34 |
| Hamed Sari-Sarraf | 2 | 109 | 18.47 |
| L. Rodney Long | 3 | 534 | 56.98 |
| Sameer Antani | 4 | 1402 | 134.03 |