Name
Abstract | ||
|---|---|---|
Machine learning algorithms allow to create highly adaptable systems, since their functionality only depends on the features of the inputs and the coefficients found during the training stage. In this paper, we present a method for building support vector machines (SVM), characterized by integer parameters and coefficients. This method is useful to implement a pattern recognition system on resource-limited hardware, where a floating-point unit is often unavailable. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/AHS.2007.6 | Edinburgh |
Keywords | Field | DocType |
floatingpoint unit,support vector machines,resource-limited adaptive hardware,integer parameter,adaptable system,pattern recognition system,machine learning algorithm,resource-limited hardware,training stage,floating point unit,svm,machine learning,adaptive system,resource allocation,support vector machine,pattern recognition | Structured support vector machine,Online machine learning,Active learning (machine learning),Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Computational learning theory,Kernel method,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2866-X | 4 | 0.45 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Anguita | 1 | 1001 | 70.58 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Stefano Pischiutta | 3 | 65 | 4.12 |