Name
Abstract | ||
|---|---|---|
In the past, automatic procedures for the design of pattern classifiers have usually used a machine of fixed size. These procedures require that prior to learning we know, or can guess, the complexity of the decision surface used by the teacher. Techniques have been discovered for changing the complexity of a pattern classifier by adding or removing parts of it. These methods, respectively called “growing” and “pruning” use well established learning rules applied alternately with the addition or removal of comput,ing equipment. Growing is thus a process by which we can increase the complexity of a classifier until it fits the problem. Pruning removes computing equipment from a classifier whose performance is satisfactory, but inefficient. The paper describes our experimental evaluation of these procedures. These studies have shown that these techniques are capable of designing a classifier which closely models a complex teacher with a minimum of storage. |
Year | DOI | Venue |
|---|---|---|
1973 | 10.1016/0020-0255(73)90029-7 | Information Sciences |
Field | DocType | Volume |
Margin (machine learning),Pattern recognition,Computer science,Artificial intelligence,Pruning (decision trees),Classifier (linguistics),Margin classifier,Decision boundary,Machine learning,Pruning | Journal | 6 |
Issue | ISSN | Citations |
2 | 0020-0255 | 1 |
PageRank | References | Authors |
0.59 | 4 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruce G. Batchelor | 1 | 15 | 3.47 |