Titlebook: Emerging Intelligent Computing Technology and Applications; 9th International Co De-Shuang Huang,Phalguni Gupta,Michael Gromiha Conference

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A Novel Feature Selection Technique for SAGE Data Classificationing technique used for measuring the expression levels of genes. Each SAGE library contains expression levels of thousands of genes (or features). It is impossible to consider all these features for classification and also the general feature selection algorithms are not efficient with this data. In

四溢

Cloudburst

暫時(shí)過(guò)來(lái)

Automated Model Selection and Parameter Estimation of Log-Normal Mixtures via BYY Harmony Learninges, model selection can be made automatically during parameter learning. In this paper, this automated model selection learning mechanism is extended to logarithmic normal (log-normal) mixtures. Actually, an adaptive gradient BYY harmony learning algorithm is proposed for log-normal mixtures. It is

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A Simple but Robust Complex Disease Classification Method Using Virtual Sample Templatege-scale biological data analysis and mining. In this work we propose a simple classification method based on virtual sample template (VST) and three distance measurements. Each VST corresponds to a subclass in training set. The label of a test sample is simply determined by measuring the similarity

令人悲傷

Biweight Midcorrelation-Based Gene Differential Coexpression Analysis and Its Application to Type IIing Pearson correlation. However, Pearson correlation is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure ‘similarity’ between gene e

傲慢人

A Hybrid Gene Selection and Classification Approach for Microarray Data Based on Clustering and PSOmicroarray data. In this approach, PSO combining with clustering method are used to perform gene selection to reduce redundancy. Firstly, genes are partitioned into a certain number of clusters by using K-means, and then PSO is used to perform gene selection from the clustered genes. Because of its

Decimate

Manifold Learner Ensembleccessfully extract intrinsic geometry underlying high-dimensional data cloud. However, there is no work considering the ensemble of local and global manifold learners to promote learning results, where such strategy has achieved great success in classification. In this paper, we propose a manifold l

llibretto

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