A new approach to ensemble learning is introduced that takes ranking rather than classification as fundamental, leading to models on the symmetric group and its cosets. The approa...
This paper considers reducing the cost of test application by permuting test vectors to improve their defect coverage. Algorithms for test reordering are developed with the goal o...
We consider the problem of unsupervised learning from a matrix of data vectors where in each row the observed values are randomly permuted in an unknown fashion. Such problems ari...
This paper deals with the Permutation Flow Shop scheduling problem with the objective of minimizing total flow time, and therefore reducing in-process inventory. A new hybrid meta...
Most current data dependence tests cannot handle loop bounds or array subscripts that are symbolic, nonlinear expressions e.g. Ani+j, where 0 j n. In this paper, we describe a d...