Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2016; 17th International C Hujun Yin,Yang Gao,Antonio J. Tallón-Ballesteros Con

AWRY

地殼

Osteons

Feature Selection in Click-Through Rate Prediction Based on Gradient Boosting,ear models combined with . regularization, which is based on previous feature engineering including feature normalization and cross combination. In this case, the model cannot realize automatic feature learning. This paper uses the ensemble method for reference and proposes a feature selection algor

Eosinophils

Encoding

Study on the Detection of Cross-Site Scripting Vulnerabilities Based on Reverse Code Audit,rough the vulnerabilities of the web application. This paper proposes an improved approach based on reverse code audit and static analysis to detect and extract the XSS vulnerabilities in the source code of the web application. In this paper, we give the theoretical definition and implementation alg

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A MapReduce-Based ELM for Regression in Big Data,er generalization performance and much fast training speed. However, the enlarging volume of dataset for training makes regression by ELM a challenging task, and it is hard to finish the training in a reasonable time or it will be out of memory. In this paper, through analyzing the theory of ELM, a

航海太平洋

討好女人

0302-9743 probabilistic modeling; particle swarm intelligence; big data analysis; applications in regression, classification, clustering, medical and biological modeling and predication; text processing and image analysis...?.978-3-319-46256-1978-3-319-46257-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Face Recognition Based on Structural Incoherence and Low Rank Projection,ing underlying subspaces. Finally, the corrected test samples are classified based on sparse representation-based classification (SRC). Experimental results on AR and Extended Yale B databases verify the efficacy and robustness of the proposed method.

ULCER

Visualizing MOOC User Behaviors: A Case Study on XuetangX,es for users. Meanwhile, clustering analysis reveals the existence of clear grouping patterns. In the second case study, we examine the influence of two user factors on the dropout rate using visualization, providing valuable guidance for maintaining student learning activities.