Suche mittels Attribut

Diese Seite stellt eine einfache Suchoberfläche zum Finden von Objekten bereit, die ein Attribut mit einem bestimmten Datenwert enthalten. Andere verfügbare Suchoberflächen sind die Attributsuche sowie der Abfragengenerator.

Suche mittels Attribut

Eine Liste aller Seiten, die das Attribut „Kurzfassung“ mit dem Wert „Human Activity Recognition (HAR) from accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perform poorly when applied to new users that were not part of the training data. Previous work has addressed this challenge by personalizing general recognition models to the motion pattern of a new user in a static batch setting. The more challenging online setting has received less attention. No samples from the target user are available in advance, but they arrive sequentially. Additionally, the user's motion pattern may change over time. Thus, adapting to new and forgetting old information must be traded off. Finally, the target user should not have to do any work to use the recognition system by labeling activities. Our work addresses this challenges by proposing an unsupervised online domain adaptation algorithm. It works by aligning the feature distribution of all the subjects, sources and target, within deep neural network layers.“ haben. Weil nur wenige Ergebnisse gefunden wurden, werden auch ähnliche Werte aufgelistet.

Hier sind 2 Ergebnisse, beginnend mit Nummer 1.

Zeige (vorherige 50 | nächste 50) (20 | 50 | 100 | 250 | 500)

    

Liste der Ergebnisse

    • Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization  + (Human Activity Recognition (HAR) from acce … Human Activity Recognition (HAR) from accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perform poorly when applied to new users that were not part of the training data. Previous work has addressed this challenge by personalizing general recognition models to the motion pattern of a new user in a static batch setting. The more challenging online setting has received less attention. No samples from the target user are available in advance, but they arrive sequentially. Additionally, the user's motion pattern may change over time. Thus, adapting to new and forgetting old information must be traded off. Finally, the target user should not have to do any work to use the recognition system by labeling activities. Our work addresses this challenges by proposing an unsupervised online domain adaptation algorithm. It works by aligning the feature distribution of all the subjects, sources and target, within deep neural network layers.target, within deep neural network layers.)
    Abgerufen von „https://sdq.kastel.kit.edu/institutsseminar/Spezial:Suche_mittels_Attribut“