Abstract
We propose a Support Vector-based methodology for learning classifiers from partially labeled data. Its novelty stands in a formulation not based on the cluster hypothesis, stating that learning algorithms should search among classifiers whose decision surface is far from the unlabeled points. On the contrary, we assume such points as specimens of uncertain labels which should lay in a region containing the decision surface. The proposed approach is tested against synthetic data sets and subsequently applied to well-known benchmarks, attaining better or at least comparable performance w.r.t. methods described in the literature.
Keywords
- Support Vector Regression
- Uncertain Measurement
- Decision Surface
- Pima Indian Diabetes
- Cluster Hypothesis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Malchiodi, D., Legnani, T. (2014). Avoiding the Cluster Hypothesis in SV Classification of Partially Labeled Data. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_4
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DOI: https://doi.org/10.1007/978-3-319-04129-2_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
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