Sökning: "Gaussian process latent variable model"

Visar resultat 1 - 5 av 7 avhandlingar innehållade orden Gaussian process latent variable model.

1. 1. Shared Gaussian Process Latent Variable Models

   Författare :Carl Henrik Ek; Oxford Brookes University; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : A fundamental task in machine learning is modeling the relationship between different observation spaces. Dimensionality reduction is the task of reducing thenumber of dimensions in a parameterization of a data-set. In this thesis we areinterested in the cross-road between these two tasks: shared dimensionality reduction. LÄS MER

3. 2. Learning local predictive accuracy for expert evaluation and forecast combination

   Författare :Oscar Oelrich; Mattias Villani; Francesco Ravazzolo; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Bayesian; forecast combination; predictive density; Gaussian process; bootstrap; Bayes factors; model selection; Bayesian predictive synthesis; nonparametric methods; power transformation; expected log predictive density; variable selection; statistik; Statistics;

   Sammanfattning : This thesis consists of four papers that study several topics related to expert evaluation and aggregation. Paper I explores the properties of Bayes factors. Bayes factors, which are used for Bayesian hypothesis testing as well as to aggregate models using Bayesian model averaging, are sometimes observed to behave erratically. LÄS MER

4. 3. Holistic Grasping: Affordances, Grasp Semantics, Task Constraints

   Författare :Martin Hjelm; Danica Kragic; Markus Vincze; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; robotics; robotic grasping; grasping; cognition; embodied cognition; computer vision; machine learning; artificial intelligence; AI; Gaussian process; Gaussian process latent variable model; GPLVM; 3D vision; point cloud features; robotik; manipulation; datorseende; maskininlärning; artificiell intelligens; kognition; Computer Science; Datalogi;

   Sammanfattning : Most of us perform grasping actions over a thousand times per day without giving it much consideration, be it from driving to drinking coffee. Learning robots the same ease when it comes to grasping has been a goal for the robotics research community for decades. LÄS MER

5. 4. Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions

   Författare :Mohamed Abdalmoaty; Håkan Hjalmarsson; Adrian Wills; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Prediction Error Method; Maximum Likelihood; Data-driven; Learning; Stochastic; Nonlinear; Dynamical Models; Non-stationary Linear Predictors; Intractable Likelihood; Latent Variable Models; Estimation; Process Disturbance; Electrical Engineering; Elektro- och systemteknik;

   Sammanfattning : Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. LÄS MER

7. 5. Spatial Mixture Models with Applications in Medical Imaging and Spatial Point Processes

   Författare :Anders Hildeman; Göteborgs universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Non-Gaussian; Bayesian level set inversion; Point processes; Substitute CT; Finite mixture models; Spatial statistics; Gaussian fields; Non-Gaussian;

   Sammanfattning : Finite mixture models have proven to be a great tool for both modeling non-standard probability distributions and for classification problems (using the latent variable interpretation). In this thesis we are building spatial models by incorporating spatially dependent categorical latent random fields in a hierarchical manner similar to that of finite mixture models. LÄS MER