Name
Abstract | ||
|---|---|---|
This paper presents a novel method for learning fro m a labeled dataset to accurately classify unknown data . The recursive algorithm, termed Recursive Hyperspheric Classification, or RHC, can accurately learn the cl asses of a labeled, n-dimensional dataset via a training method that recursively spawns a set of hyperspheres, endeavoring to separate and divide the feature spac e into partitions. This produces a comprehensive mapping of the space. These hyperspheres provide guidance for the search because they are recursively traversed. Som e benchmarking has been performed on various data set s and has shown to yield superior results to more traditional artificial methods. |
Year | Venue | Keywords |
|---|---|---|
2008 | CAINE | recursive hyperspheres,classification,rhc,center of gravity,recursive algorithm |
Field | DocType | Citations |
Feature vector,Data set,Recursion (computer science),Ramer–Douglas–Peucker algorithm,Pattern recognition,Computer science,FSA-Red Algorithm,Recursive partitioning,Artificial intelligence,Binary search algorithm,Recursion | Conference | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salyer B. Reed | 1 | 2 | 1.37 |
| Carl G. Looney | 2 | 198 | 21.58 |
| Sergiu Dascalu | 3 | 362 | 79.10 |