There are a number of supervised machine learning methods such as classiffers pretrained using restricted\ud Boltzmann machines and convolutional networks that work very well for handwritten character recognition.\ud However, they require a large amount of labeled training data to achieve good performance which\ud unlike unlabeled data is often expensive to obtain. In this paper a number of novel semi-supervised learning\ud methods for handwritten character recognition are presented based on the previous algorithms. These\ud methods are oriented towards learning from as little labeled data as possible and for this goal they use\ud unlabeled data and active learning. The proposed techniques are of varying complexity and involve simple\ud K-means clustering, feature mapping with self organizing maps, dimensionality reduction with deep\ud auto-encoders, and sub-sampling techniques. The presented algorithms outperform both a generic semisupervised\ud active learning algorithm and two well known supervised algorithms
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