Titlebook: Machine Learning: ECML 2007; 18th European Confer Joost N. Kok,Jacek Koronacki,Andrzej Skowron Conference proceedings 2007 Springer-Verlag

彎腰

Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimasitive. Most classification models in machine learning output some score of ‘positiveness’, and hence can be used as rankers. Conversely, any ranker can be turned into a classifier if we have some instance-independent means of splitting the ranking into positive and negative segments. This could be

Capture

Mining Queriesests to actual content. Even queries without clicks or answers imply important missing synonyms or content. In this talk we show several examples on how to use this information to improve the performance of search engines, to recommend better queries, to improve the information scent of the content

UTTER

Incisor

Statistical Debugging Using Latent Topic Models-Latent-Dirichlet-Allocation model. We model execution traces attributed to failed runs of a program as being generated by two types of latent topics: normal usage topics and bug topics. Execution traces attributed to successful runs of the same program, however, are modeled by usage topics only. Jo

迅速飛過

Learning Balls of Strings with Correction Queriesr, practical evidence tends to show that if the former are often available, this is usually not the case of the latter. We propose new queries, called correction queries, which we study in the framework of Grammatical Inference. When a string is submitted to the Oracle, either she validates it if it

Admonish

Nebulous

善變

Learning Metrics Between Tree Structured Data: Application to Image Recognitionpecific case of trees, some approaches focused on the learning of edit probabilities required to compute a so-called stochastic tree edit distance. However, to reduce the algorithmic and learning constraints, the deletion and insertion operations are achieved on entire subtrees rather than on single

Diatribe

Shrinkage Estimator for Bayesian Network Parametersigh variance and often overfit the training data. Laplacian correction can be used to smooth the MLEs towards a uniform distribution. However, the uniform distribution may represent an unrealistic relationships in the domain being modeled and can add an unreasonable bias. We present a shrinkage esti

exceed

Level Learning Set: A Novel Classifier Based on Active Contour Modelsactive contour models and level set methods. The proposed classifier, named . (LLS), has the ability to classify general datasets including sparse and non sparse data. It moves developments in vision segmentation into general machine learning by utilising and extending level set-based active contour