Sökning: "Support vector machine learning SVM"
Visar resultat 1 - 5 av 17 avhandlingar innehållade orden Support vector machine learning SVM.
1. Visual Representations and Models: From Latent SVM to Deep Learning
Sammanfattning : Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. LÄS MER
2. Privacy-awareness in the era of Big Data and machine learning
Sammanfattning : Social Network Sites (SNS) such as Facebook and Twitter, have been playing a great role in our lives. On the one hand, they help connect people who would not otherwise be connected before. LÄS MER
3. Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer : a study from databases, machine learning predictions to laboratory confirmations
Sammanfattning : Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Early diagnosis and better therapy response have been believed to be associated with better prognosis. CRC biomarkers are considered as precise indicators for the early diagnosis and better therapy response. LÄS MER
4. 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
5. 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
