Metric-based Model Selection Algorithm

A Metric-based Model Selection Algorithm is a Semi-Supervised Learning Algorithm that detects Inconsistent Hypothesis with unlabeled data by imposing a metric structure on hypotheses by determining the discrepancy between their predictions across the distribution of unlabeled data.

• See: Distance-based Learning.

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References

2008

• (Zhu, 2008) ⇒ Xiaojin Zhu. (2008). “Semi-Supervised Learning Literature Survey (revised edition)." Technical Report 1530, Department of Computer Sciences, University of Wisconsin, Madison.
  • Metric-based model selection (Schuurmans & Southey, 2001) is an method to detect hypotheses inconsistency with unlabeled data. We may have two hypotheses which are consistent on [math]\displaystyle{ L }[/math], for example they all have zero training set error. However they may be inconsistent on the much larger U. If so we should reject at least one of them, e.g. the more complex one if we employ Occam’s razor.

2004

• (Bilenko et al., 2004) ⇒ Mikhail Bilenko, Sugato Basu, and Raymond Mooney. (2004). “Integrating Constraints and Metric Learning in Semi-Supervised Clustering.” In: Proceedings of the twenty-first International Conference on Machine learning. doi:10.1145/1015330.1015360

2001

• (Schuurmans & Southey, 2001) ⇒ Dale Schuurmans, and Finnegan Southey. (2001). “Metric-based methods for adaptive model selection and regularization."In: Machine Learning, Special Issue on New Methods for Model Selection and Model Combination, 48. doi:10.1023/A:1013947519741
  • We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea is to impose a metric structure on hypotheses by determining the discrepancy between their predictions across the distribution of unlabeled data.

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