Affordable Access

Publisher Website

Detecting and preventing error propagation via competitive learning

Neural Networks
DOI: 10.1016/j.neunet.2012.11.001
  • Stochastic Competitive Learning
  • Semisupervised Learning
  • Error Propagation
  • Random Walk
  • Preferential Walk
  • Complex Networks
  • Computer Science


Abstract Semisupervised learning is a machine learning approach which is able to employ both labeled and unlabeled samples in the training process. It is an important mechanism for autonomous systems due to the ability of exploiting the already acquired information and for exploring the new knowledge in the learning space at the same time. In these cases, the reliability of the labels is a crucial factor, because mislabeled samples may propagate wrong labels to a portion of or even the entire data set. This paper has the objective of addressing the error propagation problem originated by these mislabeled samples by presenting a mechanism embedded in a network-based (graph-based) semisupervised learning method. Such a procedure is based on a combined random-preferential walk of particles in a network constructed from the input data set. The particles of the same class cooperate among them, while the particles of different classes compete with each other to propagate class labels to the whole network. Computer simulations conducted on synthetic and real-world data sets reveal the effectiveness of the model.

There are no comments yet on this publication. Be the first to share your thoughts.


Seen <100 times

More articles like this

Error propagation in viable cells.

on Mechanisms of Ageing and Devel... April 1979

Error propagation in viable cells

on Mechanisms of Ageing and Devel... Jan 01, 1979

10 ways to control errors in EMS. Learning from &...

on JEMS : a journal of emergency... June 2005
More articles like this..