Sökning: "curse of dimensionality"
Visar resultat 1 - 5 av 19 avhandlingar innehållade orden curse of dimensionality.
1. Breaking the Dimensionality Curse of Voronoi Tessellations
Sammanfattning : Considering the broadness of the area of artificial intelligence, interpretations of the underlying methodologies can be commonly narrowed down to either a probabilistic or a geometric point of view. Such separation is especially prevalent in more classical "pre-neural-network" machine learning if one compares Bayesian modelling with more deterministic models like nearest neighbors. LÄS MER
2. Nearest Neighbor Classification in High Dimensions
Sammanfattning : The simple k nearest neighbor (kNN) method can be used to learn from high dimensional data such as images and microarrays without any modification to the original version of the algorithm. However, studies show that kNN's accuracy is often poor in high dimensions due to the curse of dimensionality; a large number of instances are required to maintain a given level of accuracy in high dimensions. LÄS MER
3. Feature Informativeness, Curse-of-Dimensionality and Error Probability in Discriminant Analysis
Sammanfattning : This thesis is based on four papers on high-dimensional discriminant analysis. Throughout, the curse-of-dimensionality effect on the precision of the discrimination performance is emphasized. A growing dimension asymptotic approach is used for assessing this effect and the limiting error probability are taken as the performance criteria. LÄS MER
4. Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams
Sammanfattning : Big Data analytics has attracted intense interest from both academia and industry recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional data streams are being collected and curated by enterprises to support their decision-making. LÄS MER
5. Localised Radial Basis Function Methods for Partial Differential Equations
Sammanfattning : Radial basis function methods exhibit several very attractive properties such as a high order convergence of the approximated solution and flexibility to the domain geometry. However the method in its classical formulation becomes impractical for problems with relatively large numbers of degrees of freedom due to the ill-conditioning and dense structure of coefficient matrix. LÄS MER
