Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A clustering algorithm for asymmetrically related data with applications to text mining
Proceedings of the tenth international conference on Information and knowledge management
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards systematic design of distance functions for data mining applications
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficient online top-K retrieval with arbitrary similarity measures
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Learning Metrics Between Tree Structured Data: Application to Image Recognition
ECML '07 Proceedings of the 18th European conference on Machine Learning
Class-dependent dissimilarity measures for multiple instance learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Asymmetric clustering using the alpha-beta divergence
Pattern Recognition
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Many practical applications require that distance measures to be asymmetric and context-sensitive. We introduce Context-sensitive Learnable Asymmetric Dissimilarity (CLAD) measures, which are defined to be a weighted sum of a fixed number of dissimilarity measures where the associated weights depend on the point from which the dissimilarity is measured. The parameters used in defining the measure capture the global relationships among the features. We provide an algorithm to learn the dissimilarity measure automatically from a set of user specified comparisons in the form "x is closer to y than to z," and study its performance. The experimental results show that the proposed algorithm outperforms other approaches due to the context sensitive nature of the CLAD measures.