Sökning: "support vector machine"
Visar resultat 6 - 10 av 69 avhandlingar innehållade orden support vector machine.
6. On Enhancement and Quality Assessment of Audio and Video in Communication Systems
Sammanfattning : The use of audio and video communication has increased exponentially over the last decade and has gone from speech over GSM to HD resolution video conference between continents on mobile devices. As the use becomes more widespread the interest in delivering high quality media increases even on devices with limited resources. LÄS MER
7. High-Performance Computing For Support Vector Machines
Sammanfattning : Machine learning algorithms are very successful in solving classification and regression problems, however the immense amount of data created by digitalization slows down the training and predicting processes, if solvable at all. High-Performance Computing(HPC) and particularly parallel computing are promising tools for improving the performance of machine learning algorithms in terms of time. LÄS MER
8. With or without context : Automatic text categorization using semantic kernels
Sammanfattning : In this thesis text categorization is investigated in four dimensions of analysis: theoretically as well as empirically, and as a manual as well as a machine-based process. In the first four chapters we look at the theoretical foundation of subject classification of text documents, with a certain focus on classification as a procedure for organizing documents in libraries. LÄS MER
9. Computational and spatial analyses of rooftops for urban solar energy planning
Sammanfattning : In cities where land availability is limited, rooftop photovoltaic panels (RPVs) offer high potential for satisfying concentrated urban energy demand by using only rooftop areas. However, accurate estimation of RPVs potential in relation to their spatial distribution is indispensable for successful energy planning. LÄS MER
10. MaltParser -- An Architecture for Inductive Labeled Dependency Parsing
Sammanfattning : This licentiate thesis presents a software architecture for inductive labeled dependency parsing of unrestricted natural language text, which achieves a strict modularization of parsing algorithm, feature model and learning method such that these parameters can be varied independently. The architecture is based on the theoretical framework of inductive dependency parsing by Nivre \citeyear{nivre06c} and has been realized in MaltParser, a system that supports several parsing algorithms and learning methods, for which complex feature models can be defined in a special description language. LÄS MER
