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

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書目名稱Machine Learning and Data Mining in Pattern Recognition影響因子(影響力)




書目名稱Machine Learning and Data Mining in Pattern Recognition影響因子(影響力)學(xué)科排名




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書目名稱Machine Learning and Data Mining in Pattern Recognition網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Data Mining in Pattern Recognition被引頻次




書目名稱Machine Learning and Data Mining in Pattern Recognition被引頻次學(xué)科排名




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書目名稱Machine Learning and Data Mining in Pattern Recognition年度引用學(xué)科排名




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書目名稱Machine Learning and Data Mining in Pattern Recognition讀者反饋學(xué)科排名

向外才掩飾

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

窩轉(zhuǎn)脊椎動物

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