Machine learning in real-world situations sometimes starts from an initial collection of training instances; learning then proceeds off and on as new training instances come intermittently. The idea of two-phase learning has then been proposed here for effectively solving the learning problems in which training instances come in this two-stage way. Four two-phase learning algorithms based on the learning method PRISM have also been proposed for inducing rules from training instances. These alternatives form a spectrum, showing achievement of the requirement of PRISM (keeping down the number of irrelevant attributes) heavily dependent on the spent computational cost. The suitable alternative, as a trade-off between computational costs and achievement to the requirements, can then be chosen according to the request of the application domains.

It is well known that extracting parallel loops plays a significant role in designing parallelizing compilers. The execution efficiency of a loop is enhanced when the loop can be executed in parallel or partial parallel, like a DOALL or DOACROSS loop. This paper reports on the practical parallelism detector (PPD) that is implemented in PFPC (a portable FORTRAN parallelizing compiler running on OSF/1) at NCTU to concentrate on finding the parallelism available in loops. The PPD can extract the potential DOALL and DOACROSS loops in a program by invoking a combination of the ZIV test and the I test for verifying array subscripts. Furthermore, if DOACROSS loops are available, an optimization of synchronization statement is made. Experimental results show that PPD is more reliable and accurate than previous approaches.

In this paper, we investigate the optimal assignment problem of cells in PCS (Personal Communication Service) to switches on a ATM (Asynchronous Transfer Mode) network. Given cells and switches on an ATM network (whose locations are fixed and known), the problem is to group cells into clusters and assign these clusters to switches in an optimum manner. This problem is modeled as a complex integer programming problem. Since finding an optimal solution of this problem is NP-hard, a heuristic solution model consists of three phases (Cell Pre-Partitioning Phase, Cell Exchanging Phase, and Cell Migrating Phase) is proposed. Experimental results show that Cell Exchanging and Cell Migrating Phases can really reduce total cost near 44% on average.