Sökning: "probability bounds analysis"
Visar resultat 1 - 5 av 38 avhandlingar innehållade orden probability bounds analysis.
1. Approximating Stochastic Partial Differential Equations with Finite Elements: Computation and Analysis
Sammanfattning : Stochastic partial differential equations (SPDE) must be approximated in space and time to allow for the simulation of their solutions. In this thesis fully discrete approximations of such equations are considered, with an emphasis on finite element methods combined with rational semigroup approximations. LÄS MER
2. Guaranteeing Generalization via Measures of Information
Sammanfattning : During the past decade, machine learning techniques have achieved impressive results in a number of domains. Many of the success stories have made use of deep neural networks, a class of functions that boasts high complexity. Classical results that mathematically guarantee that a learning algorithm generalizes, i.e. LÄS MER
3. Performance Analysis and Deployment Techniques forWireless Sensor Networks
Sammanfattning : Recently, wireless sensor network (WSN) has become a promising technology with a wide range of applications such as supply chain monitoring and environment surveillance. It is typically composed of multiple tiny devices equipped with limited sensing, computing and wireless communication capabilities. LÄS MER
4. Statistical modelling in chemistry - applications to nuclear magnetic resonance and polymerase chain reaction
Sammanfattning : This thesis consists of two parts with the common theme of statistical modelling in chemistry. The first part is concerned with applications in nuclear magnetic resonance (NMR) spectroscopy, while the second part deals with applications in polymerase chain reaction (PCR). LÄS MER
5. Information-Theoretic Generalization Bounds: Tightness and Expressiveness
Sammanfattning : Machine learning has achieved impressive feats in numerous domains, largely driven by the emergence of deep neural networks. Due to the high complexity of these models, classical bounds on the generalization error---that is, the difference between training and test performance---fail to explain this success. LÄS MER