Name
Title | ||
|---|---|---|
Learning rules from numerical data by combining geometric and graph-theoretic approach |
Abstract | ||
|---|---|---|
We present a new method for learning rules from numerical data by using a combination of geometric and graph-theoretic methods. Three different graphs are defined to capture the geometric properties of the data-set. The graphs G(P) and G(N) capture the intra-class properties of the set of positive points P and the set of negative points N, respectively, and the graph G(P, N) captures the inter-class properties between P and N. We derive rules from these graphs by means of graph partitioning. Our method tends to give fewer rules than that obtained by decision-tree based methods. The complexity of our algorithm is O(M-3), where M = \P\ + \N\. (C) 2000 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1016/S0921-8890(00)00084-1 | ROBOTICS AND AUTONOMOUS SYSTEMS |
Keywords | Field | DocType |
learning rules,geometric methods,graph-theoretic methods | Block graph,Comparability graph,Computer science,Artificial intelligence,Split graph,Discrete mathematics,Computer vision,Geometric graph theory,Line graph,Graph product,Clique-width,Pathwidth,Machine learning | Journal |
Volume | Issue | ISSN |
33 | 2-3 | 0921-8890 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sukhamay Kundu | 1 | 306 | 104.10 |
| J. Chen | 2 | 9 | 1.23 |