Accelerated learning on the connection machine
Diane J. Cook, Lawrence B. Holder · 2002
The complexity of most machine learning techniques can be improved by transforming iterative components into their parallel equivalent. The parallel architecture of the Connection Machine provides a platform for the implementation and evaluation of parallel learning techniques. The architecture of the Connection Machine is described along with limitations of the language interface that constrain the implementation of learning programs. Connection Machine implementations of two learning programs, perceptron and AQ, are described, and their computational complexity is compared to that of the corresponding sequential versions using actual runs on the Connection Machine. Techniques for parallelizing ID3 are also analyzed, and the advantages and disadvantages of parallel implementation on the Connection Machine are discussed in the context of machine learning.>