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相关概念视频

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Data: Types and Distribution01:19

Data: Types and Distribution

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
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相关实验视频

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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一个Oracle字符的数据集,用于对机器学习算法进行基准测试.

Mei Wang1, Weihong Deng2

  • 1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

Scientific data
|January 18, 2024
PubMed
概括

研究人员介绍了Oracle-MNIST数据集,其中包括古代中国字符用于模式分类. 这一数据集由于图像噪声和多样化的写作风格,提出了独特的挑战,推动了机器学习研究.

科学领域:

  • 数字人文学科 数字人文学科
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 甲骨文提供了对中国古代文化,历史和语言的洞察.
  • 像MNIST这样的现有数据集缺乏现实世界古代脚本挑战的复杂性.

研究的目的:

  • 介绍Oracle-MNIST数据集用于对比模式分类任务.
  • 为机器学习研究提供了一个比MNIST更具挑战性的替代方案.
  • 促进对古文字的分类研究,这些古文字具有固有的噪音和风格变化.

主要方法:

  • 策划了来自10个类别的30222个灰度图像 (28x28) 的古代中国字符的数据集.
  • 为了与现有系统相兼容,对数据集进行了结构化 (27,222次培训,每班300次测试).
  • 专注于现实的挑战,包括大量的噪音和不同的写作风格.

主要成果:

  • 甲骨文-MNIST数据集的分类任务比原来的MNIST更困难.
  • 数据集捕捉了古文字中固有的独特的噪音和风格变化.
  • 能够与现有的机器学习分类器进行直接比较和集成.

结论:

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  • 甲骨文-MNIST作为一个有价值的基准,用于历史文物的模式分类.
  • 该数据集通过结合古老数据的现实挑战来推进机器学习研究.
  • 通过人工智能促进对古代文字系统的解释进行进一步的调查.