Sökning: "Hyperparameter Selection"

Visar resultat 1 - 5 av 6 avhandlingar innehållade orden Hyperparameter Selection.

1. 1. Towards Scalable Machine Learning with Privacy Protection

   Författare :Dominik Fay; Mikael Johansson; Tobias J. Oechtering; Jens Sjölund; Antti Honkela; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Privacy; Differential Privacy; Dimensionality Reduction; Image Segmentation; Hyperparameter Selection; Adaptive Optimization; Privacy Amplification; Importance Sampling; Maskininlärning; Dataskydd; Differentiell Integritet; Dimensionsreducering; Bildsegmentering; Hyperparameterurval; Adaptiv Optimering; Integritetsförstärkning; Importance Sampling; Datalogi; Computer Science; Informations- och kommunikationsteknik; Information and Communication Technology;

   Sammanfattning : The increasing size and complexity of datasets have accelerated the development of machine learning models and exposed the need for more scalable solutions. This thesis explores challenges associated with large-scale machine learning under data privacy constraints. LÄS MER

3. 2. Group-Sparse Regression : With Applications in Spectral Analysis and Audio Signal Processing

   Författare :Ted Kronvall; Statistical Signal Processing Group; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; sparse regression; group-sparsity; statistical modeling; regularization; hyperparameter-selection; spectral analysis; audio signal processing; classification; localization; multi-pitch estimation; chroma; convex optimization; ADMM; cyclic coordinate descent; proximal gradient;

   Sammanfattning : This doctorate thesis focuses on sparse regression, a statistical modeling tool for selecting valuable predictors in underdetermined linear models. By imposing different constraints on the structure of the variable vector in the regression problem, one obtains estimates which have sparse supports, i.e. LÄS MER

4. 3. Evolving intelligence : Overcoming challenges for Evolutionary Deep Learning

   Författare :Mohammed Ghaith Altarabichi; Sławomir Nowaczyk; Sepideh Pashami; Peyman Sheikholharam Mashhadi; Niklas Lavesson; Högskolan i Halmstad; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; neural networks; evolutionary deep learning; evolutionary machine learning; feature selection; hyperparameter optimization; evolutionary computation; particle swarm optimization; genetic algorithm;

   Sammanfattning : Deep Learning (DL) has achieved remarkable results in both academic and industrial fields over the last few years. However, DL models are often hard to design and require proper selection of features and tuning of hyper-parameters to achieve high performance. These selections are tedious for human experts and require substantial time and resources. LÄS MER

5. 4. Efficient training of interpretable, non-linear regression models

   Författare :Oskar Allerbo; Göteborgs universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; sparse regression; kernel regression; neural network regression; early stopping; bandwidth selection;

   Sammanfattning : Regression, the process of estimating functions from data, comes in many flavors. One of the most commonly used regression models is linear regression, which is computationally efficient and easy to interpret, but lacks in flexibility. LÄS MER

7. 5. Tree-based machine learning methods with non-life insurance applications

   Författare :Henning Zakrisson; Mathias Lindholm; Filip Lindskog; Julien Trufin; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; matematisk statistik; Mathematical Statistics;

   Sammanfattning : Non-life insurance is a field which has been data-driven for a long time, with the statistical framework behind modern-day actuarial sciences laid out at the beginning of the 20th century. Problems regarding the estimation and prediction of risk are relevant to the insurance industry specifically, but also for society as a whole. LÄS MER