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A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks...
Over the past few years extensive research has been conducted to solve classification problems with help of machine learning techniques. However, machine learning is data-driven and obtaining labeled data is often challenging in real applications. Techniques that try to overcome this burden, especially, in the presence of sparsely labeled data, can be found in the field of semi-supervised or active...
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