Essays about: "One-Class Classification"
Showing result 1 - 5 of 21 essays containing the words One-Class Classification.
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1. Multidimensional Classification of Radar Signals : A comparison between unidimensional and multidimensional classification models for pulsed radar signals
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : Radar is a technique used by many different types of remote sensing systems to keep track of their surroundings. The transmitted radar signals may carry information that could be used to infer the type of transmitter. Multiple papers have investigated the classification of pulse repetition intervals produced by radar systems. READ MORE
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2. Anomaly Detection with Machine Learning using CLIP in a Video Surveillance Context
University essay from Linköpings universitet/DatorseendeAbstract : This thesis explores the application of Contrastive Language-Image Pre-Training (CLIP), a vision-language model, in an automated video surveillance system for anomaly detection. The ability of CLIP to perform zero-shot learning, coupled with its robustness against minor image alterations due to its lack of reliance on pixel-level image analysis, makes it a suitable candidate for this application. READ MORE
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3. Semi-supervised anomaly detection in mask writer servo logs : An investigation of semi-supervised deep learning approaches for anomaly detection in servo logs of photomask writers
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Semi-supervised anomaly detection is the setting, where in addition to a set of nominal samples, predominantly normal, a small set of labeled anomalies is available at training. In contrast to supervised defect classification, these methods do not learn the anomaly class directly and should have better generalization capability as new kinds of anomalies are introduced at test time. READ MORE
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4. FEATURE EXTRACTION AND CLASSIFICATION OF TRANSIENT FAULT RECORDS
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : As the power distribution system grows and more sensors are added, more data is created every day. This data can be crucial for finding faults, but there is now so much data that it ends up being unused. This presents a valuable opportunity to gain crucial insights into the continuously expanding and increasingly complex power distribution system. READ MORE
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5. Modification of the RusBoost algorithm : A comparison of classifiers on imbalanced data
University essay from Umeå universitet/StatistikAbstract : In many situations data is imbalanced, meaning the proportion of one class is larger than the other(s). Standard classifiers often produce undesirable results when the data is imbalanced and different methods have been developed in the attempt to improve classification under such conditions. READ MORE
