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Ensembles and Casca...
Abstract
Subject headings
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- Deep learning approaches suffer from the so called interpretability problem and can therefore be very hard to visualise. Embedded Prototype Subspace Classifiers is one attempt in the field of explainable AI, which is both fast and efficient since it does not require repeated learning epochs and has no hidden layers. In this paper we investigate how ensembles and cascades of ensembles perform on some popular datasets. The focus is on handwritten data such as digits, letters and signs. It is shown how cascading can be efficiently implemented in order to both increase accuracy as well as speed up the classification.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Subspaces
- Ensembles
- Cascading
- Embedded Prototypes
- Neural Networks
- Deep Learning.
Publication and Content Type
- ref (subject category)
- art (subject category)
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