Titlebook: Rough Sets and Current Trends in Computing; 7th International Co Marcin Szczuka,Marzena Kryszkiewicz,Qinghua Hu Conference proceedings 2010

力学

书目名称Rough Sets and Current Trends in Computing影响因子(影响力)




书目名称Rough Sets and Current Trends in Computing影响因子(影响力)学科排名




书目名称Rough Sets and Current Trends in Computing网络公开度




书目名称Rough Sets and Current Trends in Computing网络公开度学科排名




书目名称Rough Sets and Current Trends in Computing被引频次




书目名称Rough Sets and Current Trends in Computing被引频次学科排名




书目名称Rough Sets and Current Trends in Computing年度引用




书目名称Rough Sets and Current Trends in Computing年度引用学科排名




书目名称Rough Sets and Current Trends in Computing读者反馈




书目名称Rough Sets and Current Trends in Computing读者反馈学科排名

Density

Adaptive Phoneme Alignment Based on Rough Set Theoryplied by updating the phoneme instances versus the optimization of the accuracy metric. The main advantage of this algorithm is the absence of a training phase allowing for wider speaker adaptability and independency. The current paper focuses on the feasibility of the task as this work is still in early research stage.

Genome

幼稚

Monitoring Parkinson’s Disease Patients Employing Biometric Sensors and Rule-Based Data Processingn doctors’ questionnaires is presented. These data constitute the input of several rule-based classifiers. It has been proved that the rough-set-based algorithm can be very suitable for automatic assessment of the PD patient’s stability/worsening state.

phlegm

破布

follicle

提炼

Vehicle Classification Based on Soft Computing Algorithmssed on vehicle mask only and those based on vehicle images. The latter ones employ Speeded Up Robust Features (SURF) and gradient images convolved with Gabor filters. Vehicle type is recognized with various classifiers: artificial neural network, K-nearest neighbors algorithm, decision tree and random forest.

glamor

Learning from Imbalanced Data in Presence of Noisy and Borderline Examplesficiently disturbed by these factors, then the focused re-sampling methods – NCR and our SPIDER2 – strongly outperformed the oversampling methods. They were also better for real-life data, where PCA visualizations suggested possible existence of noisy examples and large overlapping ares between classes.

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