Name
Abstract | ||
|---|---|---|
The term of complex data set indicates that the target data set to be recognized consists of multiple categories of pattern samples. Recognition of com- plex data set is a challenging research topic in com- puter vision and pattern recognition. The entropy reduction approach has been widely used to solve the problem of recognition of single category data set. In this paper we generalize this concept in terms of Entropy-Reduced Transformation (ERT) which contains several important properties which enable us to produce the concrete solution for the practical applications. Validation of the generalized approach is demonstrated by an example. |
Year | Venue | Keywords |
|---|---|---|
1988 | MVA | mul- tiple categories text.,index terms : pattern recognition,t,large set,ransformation,complex data set,entropy-reduced,indexing terms,pattern recognition,complex data |
Field | DocType | Citations |
Pattern recognition,Computer science,Complex data type,Feature (machine learning),Artificial intelligence,Entropy reduction,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Y. Tang | 1 | 416 | 165.12 |
| Y. Z. Qu | 2 | 3 | 0.89 |
| Ching Y. Suen | 3 | 7569 | 1127.54 |