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Related Concept Videos

Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Confidence Intervals01:21

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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.
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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.
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Uncertainty: Confidence Intervals00:54

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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...
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Confidence Interval for Estimating Population Mean01:25

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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.
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Velocity of an Object01:18

Velocity of an Object

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Understanding how an object moves along a path requires distinguishing between motion over a time span and motion at a precise moment. A useful example is a vehicle traveling along a straight and level path, where its position at any given time is known. The initial step in analyzing this motion is to measure how far the vehicle travels over a fixed time period. This measurement, called average velocity, is computed by dividing the total change in position by the duration over which the change...
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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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Clickstream Analysis for Crowd-Based Object Segmentation with Confidence.

Eric Heim, Alexander Seitel, Jonas Andrulis

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    Summary
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    This study introduces a novel method to assess crowd-sourced image segmentation quality using annotator clickstream data. The approach effectively identifies low-quality annotations and improves data merging for machine learning training.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Machine learning for image annotation faces challenges due to limited high-quality training data.
    • Crowdsourcing offers a scalable solution for data annotation but struggles with quality control.

    Purpose of the Study:

    • To develop and validate a novel method for estimating crowd-sourced image segmentation quality.
    • To improve the reliability of crowd-annotated datasets for machine learning applications.

    Main Methods:

    • Training a regressor model to predict segmentation quality using annotator clickstream data.
    • Utilizing quality estimation to filter spam and weight individual annotations during merging.

    Main Results:

    • The proposed method accurately estimates segmentation quality from clickstream data.
    • The approach outperforms existing state-of-the-art methods for merging multiple crowd annotations.
    • The regressor demonstrates effectiveness across different object classes without specific retraining.

    Conclusions:

    • Clickstream data provides a valuable signal for assessing crowd-sourced image segmentation quality.
    • This method offers a low-cost, effective solution for quality control in large-scale crowd-based annotation.
    • The findings contribute to enhancing the utility of crowd-sourced data for training machine learning models.