Name
Abstract | ||
|---|---|---|
An important issue to consider when applying machine learning to real world problems is the selection of an appropriate learning tool from the large set of available techniques. Building on our experience with the Machine Learning Toolbox, we propose a set of taxonomies that allow a domain expert, with little or no knowledge of machine learning, to choose a suitable tool for his particular application. Unlike previous classifications of learning systems, which were based on technical characteristics of these systems, ours relies on features of the applications that can be solved, such as the user's goal, available data and background knowledge, and interaction between the system and its user. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1080/08839519408945431 | APPLIED ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
machine learning | Robot learning,Data mining,Online machine learning,Instance-based learning,Semi-supervised learning,Stability (learning theory),Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 1 | 0883-9514 |
Citations | PageRank | References |
8 | 1.03 | 44 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yves Kodratoff | 1 | 581 | 172.25 |
| Vassilis Moustakis | 2 | 192 | 24.36 |
| Nicolas Graner | 3 | 22 | 3.30 |