Titlebook: Advances in Intelligent Data Analysis VIII; 8th International Sy Niall M. Adams,Céline Robardet,Jean-Fran?ois Bouli Conference proceedings

peptic-ulcer

https://doi.org/10.1007/978-94-011-0115-8lly the outputs of non-scalable search-and-score Bayesian network structure learning methods that are run on much smaller sets of variables. We assess the scalability, the performance and the stability of the procedure through several experiments on synthetic and real databases scaling up to 139 351

MERIT

Tomoharu Nakashima,Hisao Ishibuchitructure when necessary. For instance, vibration measurements can be used for monitoring the condition of a bridge. We investigate the problem of extracting features from lightweight wireless acceleration sensors. On-line algorithms for frequency domain monitoring are considered, and the resulting f

沉著

Advances in Intelligent Data Analysis VIII978-3-642-03915-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

展覽

廣大

專心

Martin Holeňa,Petr Pulc,Martin Koppe cross correlation distance. The resulting algorithm may also be used for meaningful clustering of time series subsequences, which delivers meaningless results in case of Euclidean or Pearson distance.

seroma

Julia F. Knight,Alistair H. Lachlaninvestigated in this work to balance predictive values between labeled and unlabeled training data and to improve classification accuracy. Experimental results with three popular text classification corpora show that the proper use of additional unlabeled data in this semi-supervised approach can reduce classification errors by up to 26%.

anagen

高腳酒杯

GENRE

Tomoharu Nakashima,Hisao Ishibuchiabilistic classifiers for damage detection purposes. We assess the relevance of the features in a large population of classifiers. The methods are assessed with real-life data from a wooden bridge model, where structural problems are simulated with small added weights.