Learning rate of gradient descent multi-dividing ontology algorithm
by Jianzhang Wu; Xiao Yu; Wei Gao
International Journal of Manufacturing Technology and Management (IJMTM), Vol. 28, No. 4/5/6, 2014

Abstract: As acknowledge representation model, ontology has wide applications in information retrieval and other disciplines. Ontology concept similarity calculation is a key issue in these applications. One approach for ontology application is to learn an optimal ontology score function which maps each vertex in graph into a real-value. And the similarity between vertices is measured by the difference of their corresponding scores. The multi-dividing ontology algorithm is an ontology learning trick such that the model divides ontology vertices into k parts correspond to the k classes of rates. In this paper, we propose the gradient descent multi-dividing ontology algorithm based on iterative gradient computation and yield the learning rates with general convex losses by virtue of the suitable step size and regularisation parameter selection.

Online publication date: Sun, 11-Jan-2015

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