Jafar Tanha

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—We present an algorithm for multiclass Semi-Supervised learning which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semi-supervised algorithms use approaches such as one-versus-all to convert the multiclass problem to several binary classification problems which is not optimal. We propose a multiclass(More)
In Collaborative Network (CN) environments, creation of collective understanding about both the aimed outcome and the procedure for achieving it by its members is the antecedent to any successful co-working and co-development. While a part of the common CN knowledge is pre-existing to its establishment, once the collaboration activities begin the emergent(More)
Typically, only a very limited amount of in-domain data is available for training the language model component of an Handwritten Text Recognition (HTR) system for historical data. One has to rely on a combination of in-domain and out-of-domain data to develop language models. Accordingly, domain adaptation is a central issue in language modeling for HTR. We(More)
—In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used to derive the algorithm. We apply the algorithm to animal(More)
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