de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Proceedings

Machine Learning Challenges: evaluating predictive uncertainty, visual object classification and recognising textual entailment

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83845

Quinonero Candela,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Quinonero Candela, J., Dagan I, Magnini, B., & Lauria, F. (2006). Machine Learning Challenges: evaluating predictive uncertainty, visual object classification and recognising textual entailment.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D327-9
Abstract
This book constitutes the thoroughly refereed post-proceedings of the First PASCAL (pattern analysis, statistical modelling and computational learning) Machine Learning Challenges Workshop, MLCW 2005, held in Southampton, UK in April 2005. The 25 revised full papers presented were carefully selected during two rounds of reviewing and improvement from about 50 submissions. The papers reflect the concepts of three challenges dealt with in the workshop: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; the second challenge was to recognize objects from a number of visual object classes in realistic scenes; the third challenge of recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.