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Related Concept Videos

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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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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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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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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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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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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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High-Confident Block Diagonal Analysis for Multi-View Palmprint Recognition in Unrestrained Environment.

Shuping Zhao, Lunke Fei, Tingting Cai

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    Summary
    This summary is machine-generated.

    This study introduces high-confident block diagonal analysis for multi-view palmprint recognition (HCBDA MPR) to improve identity authentication in uncontrolled environments. The method enhances accuracy by ensuring a consensus block diagonal structure across all views for robust feature preservation.

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    Area of Science:

    • Computer Science
    • Biometrics
    • Pattern Recognition

    Background:

    • Unrestrained palmprint recognition faces challenges from variable image quality, lighting, and poses in real-world scenarios.
    • Existing methods often rely on subspace structures, with block diagonal properties demonstrated for palmprint data.

    Purpose of the Study:

    • To develop a unified learning model for robust multi-view palmprint recognition.
    • To ensure a consensus block diagonal property across all views for improved feature extraction.

    Main Methods:

    • Proposed a novel high-confident block diagonal analysis for multi-view palmprint recognition (HCBDA MPR).
    • Introduced a multi-view block diagonal regularizer to enforce a consensus block diagonal structure.
    • Preserved discriminant features while learning a strict block diagonal structure across views.

    Main Results:

    • The proposed HCBDA MPR method demonstrated superior performance on real-world unrestrained palmprint databases.
    • Achieved the highest recognition accuracies compared to existing state-of-the-art methods.
    • Validated the effectiveness of the consensus block diagonal property for multi-view palmprint recognition.

    Conclusions:

    • HCBDA MPR offers a significant advancement in unrestrained palmprint recognition.
    • The method effectively addresses challenges posed by uncontrolled environments.
    • The approach provides a robust framework for identity authentication using multi-view palmprints.