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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Sampling Methods: Sample Types01:18

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Updated: Feb 25, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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$k$ -Times Markov Sampling for SVMC.

Bin Zou, Chen Xu, Yang Lu

    IEEE Transactions on Neural Networks and Learning Systems
    |July 28, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new Support Vector Machine classification (SVMC) algorithm using -times Markov sampling offers improved performance. This method reduces misclassification rates and training time, creating sparser classifiers for large datasets.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Computational Statistics

    Background:

    • Support Vector Machine (SVM) is a prevalent algorithm for classification.
    • Large training datasets lead to high algorithmic complexity in traditional SVM.
    • Existing Markov sampling methods for SVM have limitations.

    Purpose of the Study:

    • Introduce a novel SVM classification (SVMC) algorithm utilizing -times Markov sampling.
    • Evaluate the learning performance of the proposed SVMC algorithm.
    • Compare the proposed SVMC with classical SVMC and existing Markov sampling-based SVMC.

    Main Methods:

    • Implementation of the SVMC algorithm with -times Markov sampling.
    • Numerical studies on benchmark datasets.
    • Comparative analysis of misclassification rates, sampling/training time, and classifier sparsity.

    Main Results:

    • The SVMC algorithm with -times Markov sampling achieved lower misclassification rates.
    • Reduced sampling and training times were observed.
    • The resulting classifiers were sparser compared to classical and previous Markov sampling SVMC methods.
    • Performance on unbalanced and large-scale datasets was analyzed.

    Conclusions:

    • The proposed -times Markov sampling SVMC algorithm enhances efficiency and accuracy.
    • This method offers a more sparse classifier, beneficial for large-scale applications.
    • The algorithm shows promise for handling complex and imbalanced datasets.