Jafar Tanha

Learn More
—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)
Feature selection is widely used as the first stage of classification task to reduce the dimension of problem, decrease noise, improve speed and relieve memory constraints by the elimination of irrelevant or redundant features. One approach in the feature selection area is employing population-based optimization algorithms such as particle swarm(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)