Name
Abstract | ||
|---|---|---|
Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik-Chervonenkis theory, and neural networks. The presentation and the large (though nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/18.720536 | IEEE Transactions on Information Theory |
Keywords | Field | DocType |
two-class pattern classification setting,basic model,vapnik-chervonenkis theory,neural network,survey review.,index terms— classification,pattern classification-a survey,learning,statistical pattern recognition,essential feature,statistical pattern recog- nition,histogram method,analysis technique,nearest neighbor,recent result,information theory,neural nets,helium,histograms,neural networks,learning theory,indexing terms,prototypes,pattern recognition,kernel,learning artificial intelligence | k-nearest neighbors algorithm,Kernel (linear algebra),Histogram,Learning theory,Computer science,Intuition,Feature (machine learning),Artificial intelligence,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
44 | 6 | 0018-9448 |
Citations | PageRank | References |
42 | 5.43 | 136 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. R. Kulkarni | 1 | 2105 | 360.73 |
| G. Lugosi | 2 | 303 | 49.99 |
| Santosh S. Venkatesh | 3 | 381 | 71.80 |