Name
Abstract | ||
|---|---|---|
In literature, the task of learning a support vector machine for large datasets has been performed by splitting the dataset into manageable sized “partitions” and training a sequential support vector machine on each of these partitions separately to obtain local support vectors. However, this process invariably leads to the loss in classification accuracy as global support vectors may not have bee... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TBDATA.2016.2646700 | IEEE Transactions on Big Data |
Keywords | Field | DocType |
Support vector machines,Training,Big data,Kernel,Quadratic programming,Training data,Indexes | Kernel (linear algebra),Structured support vector machine,Data mining,Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Quadratic programming,Cluster analysis,Decision boundary,Big data,Machine learning | Journal |
Volume | Issue | ISSN |
3 | 1 | 2332-7790 |
Citations | PageRank | References |
7 | 0.49 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dinesh Singh | 1 | 31 | 2.49 |
| Debaditya Roy | 2 | 30 | 4.98 |
| C. Krishna Mohan | 3 | 124 | 17.83 |