By Joaquin Quiñonero-Candela (auth.), Joaquin Quiñonero-Candela, Ido Dagan, Bernardo Magnini, Florence d’Alché-Buc (eds.)
This ebook constitutes the completely refereed post-proceedings of the 1st PASCAL (pattern research, statistical modelling and computational studying) computer studying demanding situations Workshop, MLCW 2005, held in Southampton, united kingdom in April 2005.
The 25 revised complete papers provided have been rigorously chosen in the course of rounds of reviewing and development from approximately 50 submissions. The papers mirror the ideas of 3 demanding situations handled within the workshop: discovering an review base at the uncertainty of predictions utilizing classical facts, Bayesian inference, and statistical studying conception; the second one problem used to be to acknowledge gadgets from a few visible item periods in sensible scenes; the 3rd problem of spotting textual entailment addresses semantic research of language to shape a customary framework for utilized semantic inference in textual content understanding.
Read or Download Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers PDF
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Additional info for Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
If we had chosen to use only one expert for predictions we would risk obtaining an arbitrarily bad NLPD score. Figure 2 shows on a log scale a typical predictive distribution given a new test input, from our competition submission. One of the experts Hk is favored over the others; it contributes the sharp spike close to y = 220. Notice how the predictions from the experts far from the most probable spike give broader, less certain predictions. This is because the new test input is far from the training inputs for those particular experts.
Ca/∼radford/. Neal, R. M. (1996) Bayesian Learning for Neural Networks, Lecture Notes in Statistics No. 118, Springer-Verlag. Neal, R. M. and Zhang, J. (2006) “High Dimensional Classiﬁcation with Bayesian Neural Networks and Dirichlet Diﬀusion Trees”, in I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh (editors) Feature Extraction, Foundations and Applications, Studies in Fuzziness and Soft Computing, Physica-Verlag, Springer. uk/ Abstract. We describe an approach to regression based on building a probabilistic model with the aid of visualization.
The Bayesian logistic regression model predicted that three of the test cases would be in the less common class; none of these predictions were correct. These mistaken predictions may have been due to the presence of occasional extreme input values in the Gatineau dataset, which can cause a linear logistic model to produce extreme probabilities. M. Neal tanh non-linearity in the neural network’s hidden units tends to limit the eﬀect of extreme values. The good results I obtained in this competition demonstrate that Bayesian models using ARD priors can successfully deal with around a thousand inputs, without any preliminary selection of inputs.
Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers by Joaquin Quiñonero-Candela (auth.), Joaquin Quiñonero-Candela, Ido Dagan, Bernardo Magnini, Florence d’Alché-Buc (eds.)