Algorithms for clustering data
Algorithms for clustering data
SIAM Review
Making large-scale support vector machine learning practical
Advances in kernel methods
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Modern Information Retrieval
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Identification of time-varying objects on the web
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Absolute and relative clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
| Hi-index | 0.00 |
A method is described for learning a distance metric for use in object identification that does not require human supervision. It is based on two assumptions. One is that pairs of different names refer to different objects. The other is that names are arbitrary. These two assumptions justify using pairs of data items for objects with different names as “cannot-be-linked” example pairs for learning a distance metric for use in clustering ambiguous names. The metric learning is formulated using only dissimilar example pairs as a convex quadratic programming problem that can be solved much faster than a semi-definite programming problem, which generally must be solved to learn a distance metric matrix. Experiments on author identification using a bibliographic database showed that the learned metric improves identification F-measure.