Name
Abstract | ||
|---|---|---|
We describe here a method for building a support vector machine (SVM) with integer parameters. Our method is based on a branch-and-bound procedure, derived from modern mixed integer quadratic programming solvers, and is useful for implementing the feed-forward phase of the SVM in fixed-point arithmetic. This allows the implementation of the SVM algorithm on resource-limited hardware like, for example, computing devices used for building sensor networks, where floating-point units are rarely available. The experimental results on well-known benchmarking data sets and a real-world people-detection application show the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.neucom.2007.12.006 | Neurocomputing |
Keywords | Field | DocType |
feed-forward phase,support vector machine,floating-point unit,sensor networks,modern mixed integer quadratic,integer parameter,mixed integer quadratic programming (miqp),resource-limited hardware,programming solvers,branch-and-bound,sequential minimal optimization (smo),support vector machine svm,sequential minimal optimization smo,fixed-point arithmetic,real-world people-detection application,branch-and-bound procedure,svm algorithm,support vector machine (svm),mixed integer quadratic programming miqp,floating point unit,fixed point arithmetic,feed forward,sequential minimal optimization,branch and bound,sensor network | Integer,Structured support vector machine,Branch and bound,Branch and price,Support vector machine,Artificial intelligence,Sequential minimal optimization,Wireless sensor network,Mathematics,Machine learning,Benchmarking | Journal |
Volume | Issue | ISSN |
72 | 1-3 | Neurocomputing |
Citations | PageRank | References |
26 | 1.29 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Anguita | 1 | 1001 | 70.58 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Stefano Pischiutta | 3 | 65 | 4.12 |
| Sandro Ridella | 4 | 677 | 140.62 |