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 „Choosing a suitable classifier for a given dataset is an important part in the process of solving a classification problem. Meta-learning, which learns about the learning algorithms themselves, can predict the performance of a classifier without training it. The effect of different types of performance measures remains unclear, as it is hard to draw a comparison between results of existing works, which are based on different meta-datasets as well as meta-models. In this thesis, we study the predictability of different classification performance measures with meta-learning, also we compare the performances of meta-learning using different meta-regression models. We conduct experiments with meta-datasets from previous studies considering 11 meta-targets and 6 meta-models. Additionally, we study the relation between different groups of meta-features and the performance of meta-learning. Results of our experiments show that meta-targets have similar predictability and the choice of meta-model has a big impact on the performance of meta-learning.“ haben. Weil nur wenige Ergebnisse gefunden wurden, werden auch ähnliche Werte aufgelistet.

Hier sind 2 Ergebnisse, beginnend mit Nummer 1.

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Liste der Ergebnisse

    • Predictability of Classfication Performance Measures with Meta-Learning  + (Choosing a suitable classifier for a given … Choosing a suitable classifier for a given dataset is an important part in the process of solving a classification problem. Meta-learning, which learns about the learning algorithms themselves, can predict the performance of a classifier without training it. The effect of different types of performance measures remains unclear, as it is hard to draw a comparison between results of existing works, which are based on different meta-datasets as well as meta-models. In this thesis, we study the predictability of different classification performance measures with meta-learning, also we compare the performances of meta-learning using different meta-regression models. We conduct experiments with meta-datasets from previous studies considering 11 meta-targets and 6 meta-models. Additionally, we study the relation between different groups of meta-features and the performance of meta-learning. Results of our experiments show that meta-targets have similar predictability and the choice of meta-model has a big impact on the performance of meta-learning.mpact on the performance of meta-learning.)
    Abgerufen von „https://sdq.kastel.kit.edu/institutsseminar/Spezial:Suche_mittels_Attribut“