Search for dissertations about: "classifier evaluation"
Showing result 1 - 5 of 20 swedish dissertations containing the words classifier evaluation.
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1. On the Metric-based Approach to Supervised Concept Learning
Abstract : A classifier is a piece of software that is able to categorize objects for which the class is unknown. The task of automatically generating classifiers by generalizing from examples is an important problem in many practical applications. This problem is often referred to as supervised concept learning, and has been shown to be relevant in e.g. READ MORE
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2. Data Evaluation for an Electronic Nose
Abstract : An electronic nose is a device that tries to perform the same task as the human olfactory system, to be able to discriminate between different odours. In an electronic nose there are currently a number of individual sensors (typically 5-20). The response from a chemical sensor is usually measured as the change of some physical parameter, e.g. READ MORE
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3. Bioinformatics tools for discovery and evaluation of biomarkers : Applications in clinical assessment of cancer
Abstract : Cancer is a disease characterized by abnormal proliferation of cells in the body and ranks as the second leading cause of death worldwide. In order to improve cancer patient care, a major focus of cancer research is to discover biomarkers. READ MORE
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4. Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
Abstract : This thesis studies cloud capacity auto-scaling, or how to provision and release re-sources to a service running in the cloud based on its actual demand using an auto-matic controller. As the performance of server systems depends on the system design,the system implementation, and the workloads the system is subjected to, we focuson these aspects with respect to designing auto-scaling algorithms. READ MORE
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5. Sound Classification in Hearing Instruments
Abstract : A variety of algorithms intended for the new generation of hearing aids is presented in this thesis. The main contribution of this work is the hidden Markov model (HMM) approach to classifying listening environments. This method is efficient and robust and well suited for hearing aid applications. READ MORE