Int J Neural Syst - Online semi-supervised growing neural gas.

Tópicos

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Resumo

In this paper we introduce online semi-supervised growing neural gas (OSSGNG), a novel online semi-supervised classification approach based on growing neural gas (GNG). Existing semi-supervised classification approaches based on GNG require that the training data is explicitly stored as the labeling is performed a posteriori after the training phase. As main contribution, we present an approach that relies on online labeling and prediction functions to process labeled and unlabeled data uniformly and in an online fashion, without the need to store any of the training examples explicitly. We show that using on-the-fly labeling strategies does not significantly deteriorate the performance of classifiers based on GNG, while circumventing the need to explicitly store training examples. Armed with this result, we then present a semi-supervised extension of GNG (OSSGNG) that relies on the above mentioned online labeling functions to label unlabeled examples and incorporate them into the model on-the-fly. As an important result, we show that OSSGNG performs as good as previous semi-supervised extensions of GNG which rely on offline labeling strategies. We also show that OSSGNG compares favorably to other state-of-the-art semi-supervised learning approaches on standard benchmarking datasets.

Resumo Limpo

paper introduc onlin semisupervis grow neural gas ossgng novel onlin semisupervis classif approach base grow neural gas gng exist semisupervis classif approach base gng requir train data explicit store label perform posteriori train phase main contribut present approach reli onlin label predict function process label unlabel data uniform onlin fashion without need store train exampl explicit show use onthefli label strategi signific deterior perform classifi base gng circumv need explicit store train exampl arm result present semisupervis extens gng ossgng reli mention onlin label function label unlabel exampl incorpor model onthefli import result show ossgng perform good previous semisupervis extens gng reli offlin label strategi also show ossgng compar favor stateoftheart semisupervis learn approach standard benchmark dataset

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