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

2006

Proceedings

ei

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.

@proceedings{3927,
  title = {Machine Learning Challenges: evaluating predictive uncertainty, visual object classification and recognising textual entailment},
  author = {Quinonero Candela, J. and Dagan, I. and Magnini, B. and Lauria, F.},
  journal = {Proceedings of the First Pascal Machine Learning Challenges Workshop on Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment (MLCW 2005)},
  pages = {462},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Heidelberg, Germany},
  year = {2006},
  doi = {10.1007/11736790}
}