Name
Abstract | ||
|---|---|---|
Traditional machine learning and pattern recognition techniques are intimately linked to the notion of feature spaces. Adopting this view, each object is described in terms of a vector of numerical attributes and is, therefore, mapped to a point in a Euclidean (geometric) vector space, so that the distances between the points reflect the observed (dis)similarities between the respective objects. T... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TNNLS.2016.2522770 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Special issues and sections,Machine learning,Learning systems,Pattern recognition,Integer linear programming,Geometry,Deep learning,Computational modeling | Kernel (linear algebra),Vector space,Computer science,Theoretical computer science,Artificial intelligence,Euclidean geometry,Deep learning,Metric space,Perceptron,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 6 | 2162-237X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcello Pelillo | 1 | 1888 | 150.33 |
| Edwin R. Hancock | 2 | 5432 | 462.92 |
| Xuelong Li | 3 | 15049 | 617.31 |
| Vittorio Murino | 4 | 3277 | 207.20 |