Name
Abstract | ||
|---|---|---|
It is argued that in applications of concept learning from examples where not every possible category of the domain is present in the training set i.e., many real world applications, classification performance can be improved by integrating suitable discriminative and characteristic models of classification. The suggested approach is to first discriminate between the categories present in the training set and then characterize each of these categories against all possible categories. To show the viability of this approach, a number of different discriminators and characterizers are integrated and tested. In particular, a novel characterization method that makes use of the information about the statistical distribution of feature values that can be extracted from the training examples is used. By using this method it is possible to control the degree of generalization and to deal with dependencies among features. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S1088-467X(99)00010-4 | Intell. Data Anal. |
Keywords | Field | DocType |
learning from examples,concept learning,characterization,discrimination,statistical distribution | Training set,Pattern recognition,Computer science,Concept learning,Artificial intelligence,Discriminative model,Machine learning | Journal |
Volume | Issue | ISSN |
3 | 2 | Intelligent Data Analysis |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Davidsson | 1 | 34 | 3.56 |