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National Repository of Grey Literature 12 records found  previous11 - 12  jump to record: Search took 0.01 seconds. 
Semi-supervised deep learning in sequence labeling
 Páll, Juraj Eduard ; Šabata, Tomáš (advisor) ; Flusser, Martin (referee)
Sequence labeling is a type of machine learning problem that involves as- signing a label to each sequence member. Deep learning has shown good per- formance for this problem. However, one disadvantage of this approach is its requirement of having a large amount of labeled data. Semi-supervised learning mitigates this problem by using cheaper unlabeled data together with labeled data. Currently, usage of semi-supervised deep learning for sequence labeling is limited. Therefore, the focus of this thesis is on the application of semi-super- vised deep learning in sequence labeling. Existing semi-supervised deep learning approaches are examined, and approaches for sequence labeling are proposed. The proposed approaches were implemented and experimentally evaluated on named-entity recognition and part-of-speech tagging tasks.
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Data Mining Case Study in Python
 Stoika, Anastasiia ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.
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National Repository of Grey Literature : 12 records found   previous11 - 12  jump to record:
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