Essays about: "Topic interpretability"
Showing result 1 - 5 of 7 essays containing the words Topic interpretability.
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1. Applying the Shadow Rating Approach: A Practical Review
University essay from KTH/Matematik (Avd.)Abstract : The combination of regulatory pressure and rare but impactful defaults together comprise the domain of low default portfolios, which is a central and complex topic that lacks clear industry standards. A novel approach that utilizes external data to create a Shadow Rating model has been proposed by Ulrich Erlenmaier. READ MORE
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2. Anchor-based Topic Modeling with Human Interpretable Results
University essay from Linköpings universitet/Interaktiva och kognitiva systemAbstract : Topic models are useful tools for exploring large data sets of textual content by exposing a generative process from which the text was produced. Anchor-based topic models utilize the anchor word assumption to define a set of algorithms with provable guarantees which recover the underlying topics with a run time practically independent of corpus size. READ MORE
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3. Characterisation of a developer’s experience fields using topic modelling
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : Finding the most relevant candidate for a position represents an ubiquitous challenge for organisations. It can also be arduous for a candidate to explain on a concise resume what they have experience with. READ MORE
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4. Leveraging Explainable Machine Learning to Raise Awareness among Preadolescents about Gender Bias in Supervised Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. READ MORE
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5. Evolving Text Classifier Using Genetic Programming
University essay from Lunds universitet/Avdelningen för Biomedicinsk teknikAbstract : Text classification is one of the main tasks within the field of natural language processing, which has been growing significantly during the last decade with applications in different industries. Despite different approaches to text classification showing good results, such as Machine Learning and Deep Learning, their shortcomings give substance to the need for further research on other approaches. READ MORE