Jed Graef
Datos Biográficos
| ID | 5970916 |
|---|---|
| NOMBRE | Jed Graef |
| NOMBRES | Jed |
| APELLIDO | Graef |
| FIRMA | GRAEF J |
| VERIFICADO | No |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAS | 0 |
| OBRAS COMO AUTOR | 3 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 1974 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 1979 |
| ÍNDICE H | 0 |
Using distance information in the design of large multidimensional scaling experiments
The Determination of the Underlying Dimensionality of an Empirically Obtained Matrix of Proximities
A completely automatic procedure for determining the underlying dimensionality of an empirically obtained matrix of proximities is described. This technique is based on the results of an extensive Monte Carlo experiment and may be applied to matrices of order 12 to 36 inclusive. A computer program which will implement the procedure is available from the authors
Using distance information in the design of large multidimensional scaling experiments
Good nonmetric multidimensional scaling algorithms have been available for more than a decade (Kruskal, 1964), and the large number of published applications attests to their popularity and usefulness. These procedures are capable of constructing a configuration of points in a metric space using no more than the ordinal properties of the data in a matrix of dissimilariti es—thus the characterization nonmetric. Despite the fact that a large body o…
Sin obras prominentes en esta página.
The Determination of the Underlying Dimensionality of an Empirically Obtained Matrix of Proximities
A completely automatic procedure for determining the underlying dimensionality of an empirically obtained matrix of proximities is described. This technique is based on the results of an extensive Monte Carlo experiment and may be applied to matrices of order 12 to 36 inclusive. A computer program which will implement the procedure is available from the authors
Using distance information in the design of large multidimensional scaling experiments
Using distance information in the design of large multidimensional scaling experiments
Good nonmetric multidimensional scaling algorithms have been available for more than a decade (Kruskal, 1964), and the large number of published applications attests to their popularity and usefulness. These procedures are capable of constructing a configuration of points in a metric space using no more than the ordinal properties of the data in a matrix of dissimilariti es—thus the characterization nonmetric. Despite the fact that a large body o…
Computer Science (3 obras) · Mathematics (3 obras) · Artificial Intelligence (2 obras) · Data mining (2 obras) · Multidimensional scaling (2 obras) · Psychology (2 obras) · Scaling (2 obras) · Statistics (2 obras) · Advanced Statistical Methods and Models (1 obra) · Advanced Statistical Process Monitoring (1 obra)