Results on Weight Conngurations That Are Not Local Minima in Feed-forward Neural Networks
نویسنده
چکیده
Local minima in the error surfaces of feed-forward neural networks are signiicant because they may entrap gradient based training algorithms. Recent results have identiied conditions under which local minima do not occur. The present paper considers three distinct deenitions of local minimum, concluding that a new deenition, called regional minimum, corresponds most closely to intuition. Using this deenition, we analyse weight conngurations in which a hidden node is ignored or redundant and show that these are not local minima. The practical implications of this result for gradient based learning are discussed.
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