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“Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) (English Edition)”,作者:[Zheng Alan Zhao, Huan Liu]

Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) (English Edition) Kindle电子书

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作者简介

Zheng Zhao is a research statistician at the SAS Institute, Inc. His recent research focuses on designing and developing novel analytic approaches for handling large-scale data of extremely high dimensionality. Dr. Zhao is the author of PROC HPREDUCE, which is a SAS High Performance Analytics procedure for large-scale parallel variable selection. He was co-chair of the 2010 PAKDD Workshop on Feature Selection in Data Mining. He earned a Ph.D. in computer science and engineering from Arizona State University. Huan Liu is a professor of computer science and engineering at Arizona State University. Dr. Liu serves on journal editorial boards and conference program committees and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He earned a Ph.D. in computer science from the University of Southern California. With a focus on data mining, machine learning, social computing, and artificial intelligence, his research investigates problems in real-world application with high-dimensional data of disparate forms, such as social media, group interaction and modeling, data preprocessing, and text/web mining. --此文字指其他 kindle_edition 版本。

目录

Data of High Dimensionality and Challenges Dimensionality Reduction Techniques Feature Selection for Data Mining Spectral Feature Selection Organization of the Book Univariate Formulations for Spectral Feature Selection Modeling Target Concept via Similarity Matrix The Laplacian Matrix of a Graph Evaluating Features on the Graph An Extension for Feature Ranking Functions Spectral Feature Selection via Ranking Robustness Analysis for SPEC Discussions Multivariate Formulations The Similarity Preserving Nature of SPEC A Sparse Multi-Output Regression Formulation Solving the L2,1-Regularized Regression Problem Efficient Multivariate Spectral Feature Selection A Formulation Based on Matrix Comparison Feature Selection with Proposed Formulations Connections to Existing Algorithms Connections to Existing Feature Selection Algorithms Connections to Other Learning Models An Experimental Study of the Algorithms Discussions Large-Scale Spectral Feature Selection Data Partitioning for Parallel Processing MPI for Distributed Parallel Computing Parallel Spectral Feature Selection Computing the Similarity Matrix in Parallel Parallelization of the Univariate Formulations Parallel MRSF Parallel MCSF Discussions Multi-Source Spectral Feature Selection Categorization of Different Types of Knowledge A Framework Based on Combining Similarity Matrices A Framework Based on Rank Aggregation Experimental Results Discussions References Index --此文字指其他 kindle_edition 版本。

基本信息

  • 文件大小 : 14219 KB
  • 生词提示功能 : 未启用
  • 纸书页数 : 224页
  • 出版社 : Chapman and Hall/CRC (2011年12月14日)
  • 语种: : 英语
  • ASIN : B00OD4G832
  • 文本到语音转换 : 未启用
  • 用于教科书的 X-Ray: : 已启用
  • 同步设备使用情况: : 根据出版商限制,最多 4 台同步设备
  • 用户评分:
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