Sökning: "High-dimensional data"
Visar resultat 1 - 5 av 114 avhandlingar innehållade orden High-dimensional data.
1. Algorithmically Guided Information Visualization : Explorative Approaches for High Dimensional, Mixed and Categorical Data
Sammanfattning : Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. LÄS MER
2. Factor-Augmented Forecasting for High-Dimensional Data
Sammanfattning : In this thesis, we take a critical look at the factor-augmented forecast models, when a large number of time series variables available can provide the vital information for prediction. We discuss how to describe the commonality and idiosyncrasy of high-dimensional data by a handful of factors in various levels, and how to improve the predictive performance using these factors as augmented predictors. LÄS MER
3. 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
4. Order in the random forest
Sammanfattning : In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. LÄS MER
5. Resampling in network modeling of high-dimensional genomic data
Sammanfattning : Network modeling is an effective approach for the interpretation of high-dimensional data sets for which a sparse dependence structure can be assumed. Genomic data is a challenging and important example. In genomics, network modeling aids the discovery of biological mechanistic relationships and therapeutic targets. LÄS MER