Name
Abstract | ||
|---|---|---|
A novel classifier is introduced to overcome the limitations of the k-NN classification systems. It estimates the posterior class probabilities using a local Parzen window estimation with the k-nearest-neighbour prototypes (in the Euclidean sense) to the pattern to classify. A learning algorithm is also presented to reduce the number of data points to store. Experimental results in two hand-written classification problems demonstrate the potential of the proposed classification system. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0031-3203(99)00186-7 | Pattern Recognition |
Keywords | Field | DocType |
Soft nearest-neighbour classifiers,Online gradient descent,Hand-written character recognition | k-nearest neighbors algorithm,Data point,Nearest neighbour,Search algorithm,Pattern recognition,Artificial intelligence,Euclidean geometry,Classifier (linguistics),Linear classifier,Mathematics,Machine learning,Kernel density estimation | Journal |
Volume | Issue | ISSN |
33 | 12 | 0031-3203 |
Citations | PageRank | References |
17 | 0.89 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Bermejo | 1 | 87 | 12.49 |
| joan cabestany | 2 | 1276 | 143.82 |