Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

1 Introduction Parallel combinators [1] provide a systematic method for expressing parallel algorithms. For example, the map combinator can be used to imply parallelism, so map f xs means the elements of xs are stored in separate processors and the applications of f can be evaluated in parallel

, PolyAPM, the programming process is structured as a series of source-to-source transformations. Each intermediate result is a program for an Abstract Parallel Machine (APM) on which it can be executed to evaluate the transformation. We propose a decision tree of programs and corresponding APMs

and their best order, this approach is not optimal. We propose the following method for this special purpose. The compilation is cast as a sequence of program transformations. Each intermediate program runs on an Abstract Parallel Machine, while the program generated by the final transformation runs

and their best order, this approach is not optimal. To solve this problem, we propose the following method. The compilation is cast as Parallel Machine (APM), while the program generated by the final transformation runs on the target architecture. Our intermediate programs are all in the same language, Haskell

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A new set of benchmarks has been developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of ve \parallel kernel " benchmarks and three \simulated application" benchmarks. Together they mimic the computation and data movement

The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a

to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual

dynamics models which can be difficult to parallelize efficiently -- those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed--memory parallel machine which allows for message--passing of data between independently executing processors