Interactive learning using a society of models

  title={Interactive learning using a society of models},
  author={T P Minka},
  • T P Minka
  • Published 2002
Digital library access is driven by features but features are often context dependent and noisy and their relevance for a query is not always obvious This paper describes an approach for utilizing many data dependent user dependent and task dependent features in a semi automated tool Instead of requiring universal similarity measures or manual selection of relevant features the approach provides a learning algorithm for selecting and combining groupings of the data where groupings can be… CONTINUE READING
Highly Influential
This paper has highly influenced 16 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 276 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 168 extracted citations

276 Citations

Citations per Year
Semantic Scholar estimates that this publication has 276 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 36 references

M i n k a, \Vision texture for annotation

  • R W Picard
  • Journal of Multimedia Systems
  • 1995

Sasiela, \Machine learning for a t oolkit for image mining

  • R L Delanoy A N D
  • Sasiela, \Machine learning for a t oolkit for…
  • 1995

Yuille, \Region competition: Unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation

  • S C Zhu, T S Lee
  • Int. Conf. on Computer Vision
  • 1995

\Annotation of natural scenes using adaptive color segmentation

  • E Saber, A M Tekalp, R Eschbach, K Knox
  • IS&T/SPIE Electronic Imaging, F eb
  • 1995

Similar Papers

Loading similar papers…