Sharif Digital Repository

The goal of active learning is to choose the training instances intelligently, in order to make the cost of the learning process as low as possible. In proactive learning, which is an extension of active learning, several noisy labelers provide labels for each chosen sample. The proactive problem consists of three different subproblems: estimating true labels and finding their correctness probability (crowdsourcing), learning a model for finding labels of new instances (classification), and selecting samples for empowering the classifier (active learning). Since estimated labels through crowdsourcing are not certain, regular classification and active learning methods should be extended to be... 

One of the approaches used in learning theory is using information theoretic tools. The general idea of this approach is that if we show the algorithm did not memorize the dataset, we could guarantee generalization. Noisy channel model is one of the important concepts in this approach. A noisy channel is a lossy process which maps the data to a compressed format.There are two ways to use noisy channel model in literature: input compression and model compression. One of the main results of this thesis is to show that the input compression methods can not explain the generalization of algorithms (despite previous belief). On the contrast by fixing some of the problems faced in the model...