Sökning: "oövervakad träning"
Visar resultat 1 - 5 av 12 uppsatser innehållade orden oövervakad träning.
1. Intrusion Detection in IT Infrastructures using Hidden Markov Models
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In the past decades, cloud based services have developed rapidly. And as a result, cybercrimehas increased in sophistication as well as frequency. It therefore becomes vital to have solidprotection against such attacks, especially for infrastructures containing sensitive information. LÄS MER
2. Unsupervised Domain Adaptation for Regressive Annotation : Using Domain-Adversarial Training on Eye Image Data for Pupil Detection
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Machine learning has seen a rapid progress the last couple of decades, with more and more powerful neural network models continuously being presented. These neural networks require large amounts of data to train them. LÄS MER
3. Advancing Keyword Clustering Techniques: A Comparative Exploration of Supervised and Unsupervised Methods : Investigating the Effectiveness and Performance of Supervised and Unsupervised Methods with Sentence Embeddings
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Clustering keywords is an important Natural Language Processing task that can be adopted by several businesses since it helps to organize and group related keywords together. By clustering keywords, businesses can better understand the topics their customers are interested in. LÄS MER
4. Unsupervised 3D Human Pose Estimation
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. LÄS MER
5. Development of Neural Networks Using Deterministic Transforms
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Deep neural networks have been a leading research topic within the machine learning field for the past few years. The introduction of graphical processing units (GPUs) and hardware advances made possible the training of deep neural networks. Previously the training procedure was impossible due to the huge amount of training samples required. LÄS MER