Sökning: "regression models"

Visar resultat 1 - 5 av 753 avhandlingar innehållade orden regression models.

1. 1. Towards Accurate and Reliable Deep Regression Models

   Författare :Fredrik K. Gustafsson; Thomas B. Schön; Martin Danelljan; Søren Hauberg; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine Learning; Deep Learning; Regression; Probabilistic Models; Energy-Based Models; Uncertainty Estimation; Machine learning; Maskininlärning;

   Sammanfattning : Regression is a fundamental machine learning task with many important applications within computer vision and other domains. In general, it entails predicting continuous targets from given inputs. LÄS MER

3. 2. 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

4. 3. Model Selection and Sparse Modeling

   Författare :Yngve Selén; Peter Stoica; Jean-Jacques Fuchs; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; model selection; model order selection; model averaging; nested models; sparse models; Bayesian inference; MMSE estimation; MAP estimation; ML estimation; AIC; BIC; GIC; RAKE receivers; pulse compression; radar; linear models; linear regression models; Signal processing; Signalbehandling;

   Sammanfattning : Parametric signal models are used in a multitude of signal processing applications. This thesis deals with signals for which there are many candidate models, and it is not a priori known which model is the most appropriate one. LÄS MER

5. 4. Bayesian Sequential Inference for Dynamic Regression Models

   Författare :Parfait Munezero; Mattias Villani; Helga Wagner; Stockholms universitet; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Bayesian sequential inference; Dynamic regression models; Particle filter; Online prediction; Particle smoothing; Linear Bayes; Statistics; statistik;

   Sammanfattning : Many processes evolve over time and statistical models need to be adaptive to change. This thesis proposes flexible models and statistical methods for inference about a data generating process that varies over time. The models considered are quite general dynamic predictive models with parameters linked to a set of covariates via link functions. LÄS MER

7. 5. Learning predictive models from graph data using pattern mining

   Författare :Thashmee M. Karunaratne; Henrik Boström; Lars Asker; Nada Lavraˇc; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Graph Data; Pattern Mining; Classification; Regression; Predictive Models; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. LÄS MER