Sökning: "klustering"
Visar resultat 1 - 5 av 14 uppsatser innehållade ordet klustering.
1. Domain Knowledge and Representation Learning for Centroid Initialization in Text Clustering with k-Means : An exploratory study
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Text clustering is a problem where texts are partitioned into homogeneous clusters, such as partitioning them based on their sentiment value. Two techniques to address the problem are representation learning, in particular language representation models, and clustering algorithms. LÄS MER
2. Analysis of speaking time and content of the various debates of the presidential campaign : Automated AI analysis of speech time and content of presidential debates based on the audio using speaker detection and topic detection
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The field of artificial intelligence (AI) has grown rapidly in recent years and its applications are becoming more widespread in various fields, including politics. In particular, presidential debates have become a crucial aspect of election campaigns and it is important to analyze the information exchanged in these debates in an objective way to let voters choose without being influenced by biased data. LÄS MER
3. Identifying New Fault Types Using Transformer Embeddings
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Continuous integration/delivery and deployment consist of many automated tests, some of which may fail leading to faulty software. Similar faults may occur in different stages of the software production lifecycle and it is necessary to identify similar faults and cluster them into fault types in order to minimize troubleshooting time. LÄS MER
4. On the effectiveness of ß-VAEs for imageclassification and clustering : Using a disentangled representation for Transfer Learning and Semi-Supervised Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Data labeling is a critical and costly process, thus accessing large amounts of labeled data is not always feasible. Transfer Learning (TL) and Semi-Supervised Learning (SSL) are two promising approaches to leverage both labeled and unlabeled samples. LÄS MER
5. A graph representation of event intervals for efficient clustering and classification
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Sequences of event intervals occur in several application domains, while their inherent complexity hinders scalable solutions to tasks such as clustering and classification. In this thesis, we propose a novel spectral embedding representation of event interval sequences that relies on bipartite graphs. LÄS MER
