Titlebook: Machine Learning and Data Mining in Pattern Recognition; 4th International Co Petra Perner,Atsushi Imiya Conference proceedings 2005 Spring

要求

书目名称Machine Learning and Data Mining in Pattern Recognition影响因子(影响力)




书目名称Machine Learning and Data Mining in Pattern Recognition影响因子(影响力)学科排名




书目名称Machine Learning and Data Mining in Pattern Recognition网络公开度




书目名称Machine Learning and Data Mining in Pattern Recognition网络公开度学科排名




书目名称Machine Learning and Data Mining in Pattern Recognition被引频次




书目名称Machine Learning and Data Mining in Pattern Recognition被引频次学科排名




书目名称Machine Learning and Data Mining in Pattern Recognition年度引用




书目名称Machine Learning and Data Mining in Pattern Recognition年度引用学科排名




书目名称Machine Learning and Data Mining in Pattern Recognition读者反馈




书目名称Machine Learning and Data Mining in Pattern Recognition读者反馈学科排名

向外才掩饰

Incremental Classification Rules Based on Association Rules Using Formal Concept Analysisaper, we present the integration of Association rules and Classification rules using Concept Lattice. This gives more accurate classifiers for Classification. The algorithm used is incremental in nature. Any increase in the number of classes, attributes or transactions does not require the access to

Flavouring

Polydipsia

Finite Mixture Models with Negative Componentsated by several Gaussian components, however, it can not always acquire appropriate results. By cancelling the nonnegative constraint to mixture coefficients and introducing a new concept of “negative components”, we extend the traditional mixture models and enhance their performance without increas

Myocyte

MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection determining the number of clusters which best describe the data. We consider here the application of the Minimum Message length (MML) principle to determine the number of clusters. The Model is compared with results obtained by other selection criteria (AIC, MDL, MMDL, PC and a Bayesian method). Th

窝转脊椎动物

curriculum

Decrepit

Determining Regularization Parameters for Derivative Free Neural Learningg problem makes local optimization methods very attractive; however the error surface contains many local minima. Discrete gradient method is a special case of derivative free methods based on bundle methods and has the ability to jump over many local minima. There are two types of problems that are

BULLY

A Comprehensible SOM-Based Scoring System and ‘bad’ risk categories. Traditionally, (logistic) regression used to be one of the most popular methods for this task, but recently some newer techniques like neural networks and support vector machines have shown excellent classification performance. Self-organizing maps (SOMs) have existed for

Neutropenia