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  2. Sigmoid function - Wikipedia

    en.wikipedia.org/wiki/Sigmoid_function

    Sigmoid functions have domain of all real numbers, with return (response) value commonly monotonically increasing but could be decreasing. Sigmoid functions most often show a return value (y axis) in the range 0 to 1. Another commonly used range is from −1 to 1. A wide variety of sigmoid functions including the logistic and hyperbolic tangent ...

  3. Logistic function - Wikipedia

    en.wikipedia.org/wiki/Logistic_function

    The standard logistic function is the logistic function with parameters =, =, =, which yields = + = + = / / + /.In practice, due to the nature of the exponential function, it is often sufficient to compute the standard logistic function for over a small range of real numbers, such as a range contained in [−6, +6], as it quickly converges very close to its saturation values of 0 and 1.

  4. Logit - Wikipedia

    en.wikipedia.org/wiki/Logit

    The logit and probit are both sigmoid functions with a domain between 0 and 1, which makes them both quantile functions – i.e., inverses of the cumulative distribution function (CDF) of a probability distribution. In fact, the logit is the quantile function of the logistic distribution, while the probit is the quantile function of the normal ...

  5. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    The logistic function is a sigmoid function, ... — thereby matching the potential range of the linear prediction function on the right side of the equation. ...

  6. Activation function - Wikipedia

    en.wikipedia.org/wiki/Activation_function

    Range When the range of the activation function is finite, gradient-based training methods tend to be more stable, because pattern presentations significantly affect only limited weights. When the range is infinite, training is generally more efficient because pattern presentations significantly affect most of the weights.

  7. Logistic distribution - Wikipedia

    en.wikipedia.org/wiki/Logistic_distribution

    The probability density function is the partial derivative of the cumulative distribution function: (;,) = (;,) = / (+ /) = (() / + / ()) = ⁡ ().When the location parameter μ is 0 and the scale parameter s is 1, then the probability density function of the logistic distribution is given by

  8. Generalised logistic function - Wikipedia

    en.wikipedia.org/wiki/Generalised_logistic_function

    The generalized logistic function or curve is an extension of the logistic or sigmoid functions. Originally developed for growth modelling, it allows for more flexible S-shaped curves. The function is sometimes named Richards's curve after F. J. Richards, who proposed the general form for the family of models in 1959.

  9. List of mathematical abbreviations - Wikipedia

    en.wikipedia.org/wiki/List_of_mathematical...

    ran – range of a function. ... sigmoidsigmoid function. sin – sine function. sinc – sinc function. sinh – hyperbolic sine function. siv – versine function.