Name
Abstract | ||
|---|---|---|
In this paper we aim to show that instance-based classification can replace the classifier component of a rule learner and of maximum-entropy modeling, thereby improving the generalization accuracy of both algorithms. We describe hybrid algorithms that combine rule learning models and maximum-entropy modeling with instance-based classification. Experimental results show that both hybrids are able to outperform the parent algorithm. We analyze and compare the overlap in errors and the statistical bias and variance of the hybrids, their parent algorithms, and a plain instance-based learner. We observe that the successful hybrid algorithms have a lower statistical bias component in the error than their parent algorithms; the fewer errors they make are also less systematic. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11564096_19 | ECML'05 Proceedings of the 16th European conference on Machine Learning |
Keywords | Field | DocType |
parent algorithm,instance-based classification,maximum-entropy modeling,plain instance-based learner,classifier component,hybrid algorithm,lower statistical bias component,rule learner,statistical bias,successful hybrid algorithm | Learning machine,Hybrid algorithm,Computer science,Algorithm,Learning models,Artificial intelligence,Classifier (linguistics),Machine learning | Conference |
Volume | ISSN | ISBN |
3720 | 0302-9743 | 3-540-29243-8 |
Citations | PageRank | References |
2 | 0.47 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iris Hendrickx | 1 | 285 | 30.91 |
| Antal Van Den Bosch | 2 | 1038 | 132.37 |
| Antal van den Bosch | 3 | 12 | 3.30 |