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Related Concept Videos

Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Data-dependent hashing based on p-stable distribution.

Xiao Bai, Haichuan Yang, Jun Zhou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 29, 2014
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    Summary
    This summary is machine-generated.

    This study introduces data-dependent hashing using p-stable distributions, improving accuracy and efficiency for compact hash codes. The novel unsupervised and supervised methods preserve Euclidean distance and semantic similarity, outperforming existing approaches.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Science

    Background:

    • Traditional p-stable distribution hashing is data-independent.
    • Existing data-dependent methods have limitations.

    Purpose of the Study:

    • Develop data-dependent hashing using p-stable distributions.
    • Preserve Euclidean distance and semantic similarity.
    • Improve hashing accuracy and efficiency.

    Main Methods:

    • Formulated Euclidean distance preservation via variance estimation.
    • Developed a projection method using p-stable random vectors with learned weights.
    • Learned an orthogonal matrix data-dependently to minimize quantization error.
    • Extended to supervised hashing with label propagation.

    Main Results:

    • Unsupervised scheme preserves Euclidean distance with compact hash codes.
    • Supervised scheme preserves semantic similarity.
    • Outperformed state-of-the-art hashing methods in effectiveness and efficiency.

    Conclusions:

    • The proposed data-dependent hashing methods offer superior performance.
    • The approach is flexible, accommodating multiple hash tables and hash functions.
    • Effective for both unsupervised and supervised learning tasks.