ICDM07_Jin - Kent State University
... discretization algorithms: Yang and Webb; Kurgan and Cios (CAIM); Boulle (Khiops). • CAIM attempts to minimize the number of discretization intervals and at the same time to minimize the information loss. • Khiops uses Pearson’s X2 statistic to select merging consecutive intervals that minimize the ...
... discretization algorithms: Yang and Webb; Kurgan and Cios (CAIM); Boulle (Khiops). • CAIM attempts to minimize the number of discretization intervals and at the same time to minimize the information loss. • Khiops uses Pearson’s X2 statistic to select merging consecutive intervals that minimize the ...
Data Discretization
... discretization algorithms: Yang and Webb; Kurgan and Cios (CAIM); Boulle (Khiops). • CAIM attempts to minimize the number of discretization intervals and at the same time to minimize the information loss. • Khiops uses Pearson’s X2 statistic to select merging consecutive intervals that minimize the ...
... discretization algorithms: Yang and Webb; Kurgan and Cios (CAIM); Boulle (Khiops). • CAIM attempts to minimize the number of discretization intervals and at the same time to minimize the information loss. • Khiops uses Pearson’s X2 statistic to select merging consecutive intervals that minimize the ...
here
... In light of the importance of autonomy and cooperation, the multi-agent paradigm is a suitable framework for modelling the operation of these sensors and to control them in a decentralised fashion. Within this paradigm, each sensor becomes an information gathering agent. As a team, these agents dire ...
... In light of the importance of autonomy and cooperation, the multi-agent paradigm is a suitable framework for modelling the operation of these sensors and to control them in a decentralised fashion. Within this paradigm, each sensor becomes an information gathering agent. As a team, these agents dire ...
Universality classes for extreme-value statistics
... random models has already been discussed on several occasions [1], its precise relation with the so-called extreme-value statistics [2, 3] (and therefore its scope and limitations) was not previously clearly established. That such a relation should exist is however intuitively obvious: at low temper ...
... random models has already been discussed on several occasions [1], its precise relation with the so-called extreme-value statistics [2, 3] (and therefore its scope and limitations) was not previously clearly established. That such a relation should exist is however intuitively obvious: at low temper ...
Simplex algorithm
In mathematical optimization, Dantzig's simplex algorithm (or simplex method) is a popular algorithm for linear programming. The journal Computing in Science and Engineering listed it as one of the top 10 algorithms of the twentieth century.The name of the algorithm is derived from the concept of a simplex and was suggested by T. S. Motzkin. Simplices are not actually used in the method, but one interpretation of it is that it operates on simplicial cones, and these become proper simplices with an additional constraint. The simplicial cones in question are the corners (i.e., the neighborhoods of the vertices) of a geometric object called a polytope. The shape of this polytope is defined by the constraints applied to the objective function.