Learned Distance Function
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A Learned Distance Function is a Distance Function that has been Trained.
- AKA: Learned Similarity Function, Trained Distance Function.
- Context:
- It can be:
- See: Learned Similarity Function.
References
2005
- (Bilenko et al., 2005) ⇒ Mikhail Bilenko, Sugato Basu, and Mehran Sahami. (2005). “Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping.” In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM-2005).
2004
- (Schomaker & Bulacu, 2004) ⇒ Lambert Schomaker, and Marius Bulacu. (2004). “Automatic Writer Identification Using Connected-Component Contours and Edge-based Features of Uppercase Western Script.” In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6). doi:10.1109/TPAMI.2004.18
- QUOTE: A more realistic solution would entail the use of a trained distance function between two given sample feature vector. Although the idea of trained distance functions as such is appealing, preliminary experiments revealed that the results where not much better than those obtained by nearest-neighbor search. The number of contrasting classes (writers) is large, and it is difficult to find a distance function which suits all local sample configurations with a smooth margin separating ’near’ (same-identity) from ’far’ (different-identity) samples. At this moment, the combination of a comparable or lower performance with the additional cost of training efforts and additional parameters seems unattractive. However, more research is needed here, indeed.
2003
- (Bilenko and Mooney, 2003a) ⇒ Mikhail Bilenko and Raymond Mooney. (2003). “Adaptive Duplicate Detection Using Learnable String Similarity Measures.” In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003).
- (Bilenko and Mooney, 2003b) ⇒ Mikhail Bilenko, and Raymond Mooney. (2003). “Employing Trainable String Similarity Metrics for Information Integration.” In: Proceedings of the IJCAI-2003 Workshop on Information Integration.