This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance - a
Graph-Theoretic Techniques for Macromolecular Docking
β Scribed by Gardiner, E.J.; Willett, P.; Artymiuk, P.J.
- Book ID
- 126799096
- Publisher
- American Chemical Society
- Year
- 2000
- Tongue
- English
- Weight
- 530 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0095-2338
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
We consider the problem of identifying the dimension in which a sample of data points lives, when only their interpoint distances are known. We study as a random variable the average ''reach'' of vertices in the k-nearest-neighbors graph associated to the interpoint distance matrix, and we show how
In this paper a graph theoretical elaboration of the stochastic cumulative scaling model of Mokken (1970) is given to determine: (a) cumulative scales of vertices on the basis of their relations with other vertices in a simple graph; and (b) cumulative scales of relations in a multigraph. In Stokma