On classification with bags, groups and sets
By Veronika Cheplygina, David Martinus Johannes Tax, Marco Loog. 2014
39 citations recorded by the source corpus.
Many classification tasks are not entirely suitable for supervised learning.Instead of individual feature vectors, bags of feature vectors can be considered.Many learning scenarios with bags in training and/or test phase have been proposed.We provide an overview and taxonomy of these learning scenarios. Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Amos J. Storkey
- Chelsea Finn
- Nando de Freitas
- Harrison Edwards
- Misha Denil
- Quoc V. Le
- Yoshua Bengio
In Computer Vision and Pattern Recognition
- Amos J. Storkey
- Luc Van Gool
- Dacheng Tao
- Sergey Levine
- Trevor Darrell
- Bernt Schiele
- Jitendra Malik
- Quoc V. Le
Citations in the corpus
- Towards a Neural Statistician (2016, background): “There is previous work on classifying sets in multiple-instance learning, for a useful survey see Cheplygina et al. [2].”
- From Group to Individual Labels Using Deep Features (2015, background): “The literature on this topic is vast and there is often disagreement in terminology—for extensive surveys see [5, 9] least 1 positive instance and that negative bags contain only negative instances.”