Abstract. Reject option is a technique used to improve classifier’s reliability in decision support systems. It consists on withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue have been concerned with implementing a reject option by endowing a supervised learning scheme (e.g., MLP, LVQ or SVM) with a re-ject mechanism. In this paper we introduce variants of the Self-Organizing Map (SOM), originally an unsupervised learning scheme, to act as supervised classi-fiers with reject option, and compare their performances with that of the MLP classifier
On-line learning is a training paradigm that allows the processing of constant data flows, so that l...
Active learning is an important technique to reduce the number of labeled examples in supervised lea...
Active learning is an important technique to reduce the number of labeled examples in supervised lea...
Villmann T, Kaden M, Bohnsack A, et al. Self-Adjusting Reject Options in Prototype Based Classificat...
International audienceWe present in this paper a new approach of supervised self organizing map (SOM...
International audienceThis paper focuses on binary classification with reject op-tion, enabling the ...
International audienceThis paper focuses on binary classification with reject op-tion, enabling the ...
International audienceThis paper focuses on binary classification with reject op-tion, enabling the ...
In this paper, the problem of implementing the reject option in support vector machines (SVMs) is ad...
Classifier performance can be severely affected when new unseen classes are present, or the conditio...
The interest of reject for classifier optimization has been shown many times. The diversity of the a...
International audienceIn supervised classification, the learning process typically trains a classifi...
International audienceIn supervised classification, the learning process typically trains a classifi...
International audienceIn supervised classification, the learning process typically trains a classifi...
Classification Abstract — In this paper we are interested in classification problems with a performa...
On-line learning is a training paradigm that allows the processing of constant data flows, so that l...
Active learning is an important technique to reduce the number of labeled examples in supervised lea...
Active learning is an important technique to reduce the number of labeled examples in supervised lea...
Villmann T, Kaden M, Bohnsack A, et al. Self-Adjusting Reject Options in Prototype Based Classificat...
International audienceWe present in this paper a new approach of supervised self organizing map (SOM...
International audienceThis paper focuses on binary classification with reject op-tion, enabling the ...
International audienceThis paper focuses on binary classification with reject op-tion, enabling the ...
International audienceThis paper focuses on binary classification with reject op-tion, enabling the ...
In this paper, the problem of implementing the reject option in support vector machines (SVMs) is ad...
Classifier performance can be severely affected when new unseen classes are present, or the conditio...
The interest of reject for classifier optimization has been shown many times. The diversity of the a...
International audienceIn supervised classification, the learning process typically trains a classifi...
International audienceIn supervised classification, the learning process typically trains a classifi...
International audienceIn supervised classification, the learning process typically trains a classifi...
Classification Abstract — In this paper we are interested in classification problems with a performa...
On-line learning is a training paradigm that allows the processing of constant data flows, so that l...
Active learning is an important technique to reduce the number of labeled examples in supervised lea...
Active learning is an important technique to reduce the number of labeled examples in supervised lea...