A Neural Network Approach to Similarity Learning

Authors:
Stefano Melacci;Lorenzo Sarti;Marco Maggini;Monica Bianchini
Affiliations:
DII, Università degli Studi di Siena, Siena, Italy 53100;DII, Università degli Studi di Siena, Siena, Italy 53100;DII, Università degli Studi di Siena, Siena, Italy 53100;DII, Università degli Studi di Siena, Siena, Italy 53100
Venue:
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Year:
2008

Citing 4
Cited 0

A probabilistic framework for semi-supervised clustering

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
Learning a Mahalanobis Metric from Equivalence Constraints

The Journal of Machine Learning Research
Processing directed acyclic graphs with recursive neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel neural network model, called similarity neural network (SNN), designed to learn similarity measures for pairs of patterns. The model guarantees to compute a non negative and symmetric measure, and shows good generalization capabilities even if a very small set of supervised examples is used for training. Preliminary experiments, carried out on some UCI datasets, are presented, showing promising results.