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Abstract : |
We report on a series of experiments in which three machine-learning algorithms are trained to hyphenate English words, viz. the backpropagation algorithm, the cascadebackpropagation algorithm, and the information-gain-tree algorithm (IG-Tree). English hyphenation is an interesting testing ground for machine learning: it has a few underlying principles, and a large amount of exceptions. A successful learning algorithm must be able to deal with both. The three learning algorithms vary in the way they compress the training material: by extracting a limited number of general rules (greedy learning), or by storing large amounts of instances (lazy learning). Our experiments show that the lazy learning algorithm (i.e., the IG-Tree algorithm) scales up better than the two greedy learning algorithms (the backpropagation algorithm and the cascade-backpropagation algorithm). Moreover, our results call for including very large problem sets in collections of machine-learning benchmark problems. 1, |
