Titlebook: Intelligent Computing Methodologies; 10th International C De-Shuang Huang,Kang-Hyun Jo,Ling Wang Conference proceedings 2014 Springer Inter

incredulity

环形

Automatic Non-negative Matrix Factorization Clustering with Competitive Sparseness Constraintse conventional determination method may be to test a number of candidates and select the optimal one with the best clustering performance. However, such strategy of repetition test is obviously time-consuming. In this paper, we propose a novel efficient algorithm called the automatic NMF clustering

hardheaded

Fusing Decision Trees Based on Genetic Programming for Classification of Microarray DatasetsGP with three voting methods: min, max and average. In this way, each individual of GP acts as an ensemble system. When the evolution process of GP ends, the final ensemble committee is selected from the last generation by a forward search algorithm. GPES is evaluated on microarray datasets, and res

漂白

A Study of Data Classification and Selection Techniques for Medical Decision Support Systemsking decisions more accurate and effective, minimizing medical errors, improving patient safety and reducing costs. Our research study indicates that it is difficult to compare different artificial intelligence techniques which are utilised to solve various medical decision-making problems using dif

Culmination

A Reduction SVM Classification Algorithm Based on Adaptive AP Clustering Granulationd the higher classification speed. In order to reduce the number of SVs but without losing of generalization performance, a new algorithm called Classification Algorithm of Support Vector Machine based on Adaptive Affinity Propagation clustering Granulation (CSVM-AAPG) is proposed, which employs Aff

BRIDE

Learning Automata Based Cooperative Student-Team in Tutorial-Like Systemed using LA. The new philosophy of a student is that he acquires knowledge not only from teacher, but also from team-workers. The self-examination indicator makes it possible for students to evaluate his learning outcomes. The below normal learner adopts the collective intelligence to improve himsel

Libido

Clustering-Based Latent Variable Models for Monocular Non-rigid 3D Shape Recoverys as possible. Given a known dataset to learn a model, existing latent variable models (LVMs) fail to focus on how to attain labeled samples. In this paper, we propose novel clustering-based LVMs in which we automatically select representative samples to be the labeled ones. To this end, G-means alg

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