GAR: An efficient and scalable graph-based activity regularization for semi-supervised learning
نویسندگان
چکیده
منابع مشابه
GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning
In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised portion of the training. Adjacency of the examples is inferred using the predictions of a neural network model which is first initialized by a supervised pretraining. These predictions are then updated according to a nove...
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Data often comes in the form of a graph. When it does not, it often makes sense to represent it as a graph for learning tasks that rely on the similarities or relationships between data points. As data size grows, traditional methods for learning on graphs often become computationally intractable in terms of time and space requirements. We describe new methods for graph-based clustering and sem...
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Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many data mining applications in recent years. This paper addresses the semi-supervised learning problem by developing a semiparametric regularization based approach, which attempts to discover the marginal distribution of...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2018
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.03.028