Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Similarity Learning for Nearest Neighbor Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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Many people have tried to learn Mahanalobis distance metric in kNN classification by considering the geometry of the space containing examples. However, similarity may have an edge specially while dealing with text e.g. Information Retrieval. We have proposed an online algorithm, SiLA (Similarity learning algorithm) where the aim is to learn a similarity metric (e.g. cosine measure, Dice and Jaccard coefficients) and its variation eSiLA where we project the matrix learnt onto the cone of positive, semidefinite matrices. Two incremental algorithms have been developed; one based on standard kNN rule while the other one is its symmetric version. SiLA can be used in Information Retrievalwhere the performance can be improved by using user feedback.