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Adaptation for Evolving Domains

Bitarafan, Adeleh | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47593 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Soleymani Baghshah, Mahdieh
  7. Abstract:
  8. Until now many domain adaptation methods have been proposed. A major limitation of almost all of these methods is their assumption that all test data belong to a single stationary target distribution and a large amount of unlabeled data is available for modeling this target distribution. In fact, in many real world applications, such as classifying scene image with gradually changing lighting and spam email identification, data arrives sequentially and the data distribution is continuously evolving. In this thesis, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced and propose the Evolving Domain Adaptation (EDA) method to classify arriving target domain data. We assume that the available data for the source domain (training data) are labeled but the examples of the target domain (test data) can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. In the proposed method, we first find a new feature space in which the source domain and the current target domain are approximately indistinguishable to use all of available data for finding the classifier. Therefore, source and target domain data are similarly distributed in the new feature space and we use a semi-supervised classification method to utilize both the unlabeled data of the target domain and the labeled data of the source domain. Since test data arrives sequentially, we propose an incremental approach both for finding the new feature space and for semi-supervised classification. Experiments on several real datasets demonstrate the superiority of our proposed method in comparison to the other recent methods
  9. Keywords:
  10. Domain Adaptation ; Semi-Supervised Learning ; Online Learning ; Manifold Assumption ; Evolving Domains

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