In this paper, the multiclass supervised training problem is considered when a discrete set of classes is assumed. Upon generating affine models for finite data sets, we have observed the invariance of certain measures of performance after a trained classifier has been presented with test data of unknown classification. Specifically, after constructing mappings between training vectors and their desired targets, the class membership and ranking of test data has been found to remain either invariant or close to invariant under a transformation of the set of target vectors. Therefore, we derive conditions explaining how this type of invariance can arise when the multiclass problem is phrased in the context of linear networks. A bioinformatics example is then presented in order to demonstrate various principles outlined in this work.

Parallel computers have matured to the point where they are capable of running a significant production workload. Characterizing this workload, however, is far more complicated than for the single-processor case. Besides the varying number of processors that may be invoked, the nodes themselves may provide differing computational resources (memory size, for example). In addition, the batch schedulers may introduce further categories of service which must be considered in the analysis.The Cornell Theory Center (CTC) put a 512-node IBM SP2 system into production in early 1995. Extended traces of batch jobs began to be collected in mid-1995 when the usage base became sufficiently large. This paper offers an analysis of this early batch workload.