相关实验视频

Updated: Sep 11, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

可靠的编程软弱监督与标签概率的信心区间

Veronica Alvarez, Santiago Mazuelas, Steven An

    IEEE transactions on pattern analysis and machine intelligence
    |August 11, 2025
    PubMed
    概括

    这项研究引入了一种用于编程弱监督的新方法,提高了标签预测可靠性. 它为标签概率提供置信区间,解决当前技术的局限性.

    更多相关视频

    Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays06:13

    Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

    Published on: February 21, 2020

    6.7K
    A Tactile Automated Passive-Finger Stimulator TAPS19:44

    A Tactile Automated Passive-Finger Stimulator TAPS

    Published on: June 3, 2009

    13.8K

    相关概念视频

    Confidence Intervals01:21

    Confidence Intervals

    An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
    A...
    7.1K
    Interpretation of Confidence Intervals01:19

    Interpretation of Confidence Intervals

    A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
    Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
    Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
    6.5K
    Prediction Intervals01:03

    Prediction Intervals

    The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
    However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
    2.3K
    Uncertainty: Confidence Intervals00:54

    Uncertainty: Confidence Intervals

    The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
    4.7K
    Confidence Interval for Estimating Population Mean01:25

    Confidence Interval for Estimating Population Mean

    A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
    A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
    8.0K
    Testing a Claim about Population Proportion01:24

    Testing a Claim about Population Proportion

    A complete procedure for testing a claim about a population proportion is provided here.
    There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
    The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
    3.4K