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In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse auto encoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse auto encoder to learn the features of the data. Secondly,...
In this paper we describe a model for classifying binary data using classifiers based on Bernoulli mixture models. We show how Bernoulli mixtures can be used for feature extraction and dimensionality reduction of raw input data. The extracted features are then used for training a classifier for supervised labeling of individual sample points. We have applied this method to two different types of datasets,...
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