Bootstrap sampling

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  • Bài giảng "Học máy - Chương 2: Đánh giá hiệu năng học máy" giới thiệu tới người đọc các nội dung: Đánh giá hiệu năng học máy, các phương pháp đánh giá hiệu năng học máy, Bootstrap sampling, tập tối ưu, các tiêu chí đánh giá tập tối ưu, tính chính xác, ma trận nhầm lẫn,... Mời các bạn cùng tham khảo nội dung chi tiết.

    pdf23p nhasinhaoanh_09 13-10-2015 44 8   Download

  • Statistics has evolved into a very important discipline that is applied in many fields. In the modern age of computing, both statistical methodology and its applications are expanding greatly. Among the many areas of application, we (Friis and Chernick) have direct experience in the use of statistical methods to military problems, space surveillance, experimental design, data validation, forecasting workloads, predicting the cost and duration of insurance claims, quality assurance, the design and analysis of clinical trials, and epidemiologic studies....

    pdf419p thuytienvang_1 31-10-2012 22 4   Download

  • Iterative bootstrapping algorithms are typically compared using a single set of handpicked seeds. However, we demonstrate that performance varies greatly depending on these seeds, and favourable seeds for one algorithm can perform very poorly with others, making comparisons unreliable. We exploit this wide variation with bagging, sampling from automatically extracted seeds to reduce semantic drift. However, semantic drift still occurs in later iterations.

    pdf9p hongphan_1 14-04-2013 21 2   Download

  • In this chapter, you will learn how to: Design simulation frameworks to solve a variety of problems in finance, explain the difference between pure simulation and bootstrapping, describe the various techniques available for reducing Monte Carlo sampling variability, implement a simulation analysis in EViews.

    ppt26p estupendo3 18-08-2016 9 2   Download

  • Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we present an unsupervised bootstrapping approach for WSD which exploits huge amounts of automatically generated noisy data for training within a supervised learning framework. The method is evaluated using the 29 nouns in the English Lexical Sample task of SENSEVAL2.

    pdf8p bunbo_1 17-04-2013 18 1   Download


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