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Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Sound Intensity Level00:53

Sound Intensity Level

Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and hence a...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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Systematic or...
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Related Experiment Video

Updated: May 16, 2026

Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage
07:13

Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage

Published on: February 10, 2023

Noise tolerance under risk minimization.

Naresh Manwani, P S Sastry

    IEEE Transactions on Cybernetics
    |November 30, 2012
    PubMed
    Summary
    This summary is machine-generated.

    This study examines noise-tolerant learning for classifiers, finding that risk minimization with 0-1 loss is highly noise-tolerant. Other loss functions show limited tolerance to noisy data, impacting classifier accuracy.

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    Last Updated: May 16, 2026

    Modified Experimental Conditions for Noise-Induced Hearing Loss in Mice and Assessment of Hearing Function and Outer Hair Cell Damage
    07:13

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    Published on: February 10, 2023

    Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
    11:18

    Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

    Published on: September 12, 2014

    Area of Science:

    • Machine Learning
    • Computational Statistics

    Background:

    • Investigates noise-tolerant learning for classifiers, assuming an ideal noise-free training set corrupted by label noise.
    • Defines noise tolerance as consistent classifier accuracy on noise-free data, regardless of training data noise.

    Discussion:

    • Analyzes noise-tolerance properties of risk minimization across various loss functions.
    • Highlights that the probability of label corruption can be feature-dependent, reflecting real-world noisy data scenarios.

    Key Insights:

    • Risk minimization with 0-1 loss demonstrates significant noise-tolerance.
    • Risk minimization with squared error loss is only tolerant to uniform noise.
    • Other loss functions under risk minimization exhibit poor noise tolerance.

    Outlook:

    • Discusses the theoretical implications of these findings for developing robust machine learning algorithms.
    • Suggests avenues for future research in designing classifiers resilient to label noise.