Selecting Representative Data Sets
By Tomás Borovicka, Marcel Jirina Jr., Pavel Kordík. 2012
100 citations recorded by the source corpus.
A training set is a special set of labeled data providing known information that is used in the supervised learning to build a classification or regression model. We can imagine each training instance as a feature vector together with an appropriate output value (label, class identifier). A supervised learning algorithm deduces a classification or regression function from the given training set. The deduced classification or regression function should predict an appropriate output value for any input vector. The goal of the training phase is to estimate parameters of a model to predict output values with a good predictive performance in real use of the model.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks
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In Artificial Intelligence
- Ciarán Hughes
- Patrick Pérez
- Karl Amende
- Stefan Milz
- Christian Witt
- Martin Simon
- Senthil Kumar Yogamani
- Michal Uřičář
In Computer Vision and Pattern Recognition
- Senthil Kumar Yogamani
- Ganesh Sistu
- Stefan Milz
- Sumanth Chennupati
- Karl Amende
- Michal Uřičář
- Martin Simon
- Hazem Rashed
In Evaluation & Benchmarks
- Ciarán Hughes
- Patrick Pérez
- Karl Amende
- Stefan Milz
- Christian Witt
- Martin Simon
- Senthil Kumar Yogamani
- Sumanth Chennupati
Citations in the corpus
- WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “ing conventions, which we will refer to first (we follow the definitions provided in [4]).”