Essays about: "Online supervised learning"
Showing result 1 - 5 of 40 essays containing the words Online supervised learning.
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1. Effectivisation of keywords extraction process : A supervised binary classification approach of scraped words from company websites
University essay from Umeå universitet/Institutionen för matematik och matematisk statistikAbstract : In today’s digital era, establishing an online presence and maintaining a well-structured website is vitalfor companies to remain competitive in their respective markets. A crucial aspect of online success liesin strategically selecting the right words to optimize customer engagement and search engine visibility. READ MORE
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2. Sentimental Analysis of CyberbullyingTweets with SVM Technique
University essay fromAbstract : Background: Cyberbullying involves the use of digital technologies to harass, humiliate, or threaten individuals or groups. This form of bullying can occur on various platforms such as social media, messaging apps, gaming platforms, and mobile phones. With the outbreak of covid-19, there was a drastic increase in utilization of social media. READ MORE
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3. An unsupervised method for Graph Representation Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Internet services, such as online shopping and chat apps, have been spreading significantly in recent years, generating substantial amounts of data. These data are precious for machine learning and consist of connections between different entities, such as users and items. READ MORE
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4. Online Unsupervised Domain Adaptation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Learning models have seen great application in demanding tasks such as machine translation and autonomous driving. However, building such models has proved challenging, both from a computational perspective and due to the requirement of a plethora of annotated data. READ MORE
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5. Continual Learning and Biomedical Image Data : Attempting to sequentially learn medical imaging datasets using continual learning approaches
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : While deep learning has proved to be useful in a large variety of tasks, a limitation remains of needing all classes and samples to be present at the training stage in supervised problems. This is a major issue in the field of biomedical imaging since keeping samples in the training sets consistently is often a liability. READ MORE