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  2. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    DBSCAN executes exactly one such query for each point, and if an indexing structure is used that executes a neighborhood query in O(log n), an overall average runtime complexity of O(n log n) is obtained (if parameter ε is chosen in a meaningful way, i.e. such that on average only O(log n) points are returned).

  3. Random sample consensus - Wikipedia

    en.wikipedia.org/wiki/Random_sample_consensus

    Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence [clarify] on the values of the estimates.

  4. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  5. OPTICS algorithm - Wikipedia

    en.wikipedia.org/wiki/OPTICS_algorithm

    The parameter ε is, strictly speaking, not necessary. It can simply be set to the maximum possible value. When a spatial index is available, however, it does play a practical role with regards to complexity. OPTICS abstracts from DBSCAN by removing this parameter, at least to the extent of only having to give the maximum value.

  6. Learning rate - Wikipedia

    en.wikipedia.org/wiki/Learning_rate

    The learning rate and its adjustments may also differ per parameter, in which case it is a diagonal matrix that can be interpreted as an approximation to the inverse of the Hessian matrix in Newton's method. [5] The learning rate is related to the step length determined by inexact line search in quasi-Newton methods and related optimization ...

  7. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.

  8. Jennifer Grey Recalls 'Smoking a Lot of Weed' Before Sex ...

    www.aol.com/lifestyle/jennifer-grey-recalls...

    Jennifer Grey looked back on how a sex scene with Patrick Swayze — that was ultimately cut from 1984’s Red Dawn — was derailed by him being drunk, and her "smoking a lot of weed" at the time ...

  9. Local outlier factor - Wikipedia

    en.wikipedia.org/wiki/Local_outlier_factor

    Local Outlier Probability (LoOP) [9] is a method derived from LOF but using inexpensive local statistics to become less sensitive to the choice of the parameter k. In addition, the resulting values are scaled to a value range of [0:1] .