SAS9.4SID(2019)到9月份带EM.txt;
2019-12-21 19:26:50 6KB SAS SI
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Windows10系统不能识别新增光驱位机械硬盘的解决方法,安装amd_sata_controller驱动程序 。 右键点击系统桌面左下角的【开始】,在开始菜单中点击【设备管理器(M)】
2019-12-21 18:58:24 2.31MB AMD SATA
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HP DL 160 G6 Server 2003/2008 上的 HP Smart Array B110i SATA RAID 控制器 亲测可用
2019-12-21 18:51:53 537KB DL160 G6 B110i B110i
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本文件是一个SATA转USB方案,原理图所用软件是Cadence,请使用10.1以上版本打开,资源来自网络,请仅做参考。
2019-12-21 18:51:01 279KB SATA转USB 原理图 Cadence
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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition。 Learn how to produce predictive models and prepare presentation-quality graphics in record time with Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition. If you are a graduate student, researcher, or statistician interested in predictive modeling; a data mining expert who wants to learn SAS Enterprise Miner; or a business analyst looking for an introduction to predictive modeling using SAS Enterprise Miner, you'll be able to develop predictive models quickly and effectively using the theory and examples presented in this book. Author Kattamuri Sarma offers the theory behind, programming steps for, and examples of predictive modeling with SAS Enterprise Miner, along with exercises at the end of each chapter. You'll gain a comprehensive awareness of how to find solutions for your business needs. This second edition features expanded coverage of the SAS Enterprise Miner nodes, now including File Import, Time Series, Variable Clustering, Cluster, Interactive Binning, Principal Components, AutoNeural, DMNeural, Dmine Regression, Gradient Boosting, Ensemble, and Text Mining. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner.
2019-12-21 18:50:59 29.28MB SAS 文本分析 数据挖掘
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包含《应用多元分析》的课本、ppt讲义、原始数据文件、sas程序及数据库等资料。
2019-11-25 01:05:30 6.65MB SAS
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