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Hofer, 2015 - Google Patents

Adapting a classification rule to local and global shift when only unlabelled data are available

Hofer, 2015

Document ID
17402462485557076919
Author
Hofer V
Publication year
Publication venue
European Journal of Operational Research

External Links

Snippet

For evolving populations the training data and the test data need not follow the same distribution. Thus, the performance of a prediction model will deteriorate over the course of time. This requires the re-estimation of the prediction model after some time. However, in …
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Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/6269—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on the distance between the decision surface and training patterns lying on the boundary of the class cluster, e.g. support vector machines
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