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  2. Category:Optimization algorithms and methods - Wikipedia

    en.wikipedia.org/wiki/Category:Optimization...

    Learning rate; Least squares; Least-squares spectral analysis; Lemke's algorithm; Level-set method; Levenberg–Marquardt algorithm; Lexicographic max-min optimization; Lexicographic optimization; Limited-memory BFGS; Line search; Linear-fractional programming; Lloyd's algorithm; Local convergence; Local search (optimization) Luus–Jaakola

  3. 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. [2] [3]

  4. Jenks natural breaks optimization - Wikipedia

    en.wikipedia.org/wiki/Jenks_natural_breaks...

    The Jenks optimization method, also called the Jenks natural breaks classification method, is a data clustering method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class's average deviation from the class mean, while maximizing each class's deviation from the means of the ...

  5. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    In 1997, the practical performance benefits from vectorization achievable with such small batches were first explored, [13] paving the way for efficient optimization in machine learning. As of 2023, this mini-batch approach remains the norm for training neural networks, balancing the benefits of stochastic gradient descent with gradient descent .

  6. Bayesian optimization - Wikipedia

    en.wikipedia.org/wiki/Bayesian_optimization

    Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.

  7. Relevance vector machine - Wikipedia

    en.wikipedia.org/wiki/Relevance_vector_machine

    In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. [1] A greedy optimisation procedure and thus fast version were subsequently developed.

  8. One-class classification - Wikipedia

    en.wikipedia.org/wiki/One-class_classification

    In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, [1] although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary.

  9. Simulation-based optimization - Wikipedia

    en.wikipedia.org/wiki/Simulation-based_optimization

    Fig.1 Classification of simulation based optimization according to variable types Optimization exists in two main branches of operations research: Optimization parametric (static) – The objective is to find the values of the parameters, which are “static” for all states, with the goal of maximizing or minimizing a function.