enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. 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] Hyperparameter optimization determines the set of ...

  3. Hyperparameter (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(machine...

    In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).

  4. Auto-WEKA - Wikipedia

    en.wikipedia.org/wiki/Auto-WEKA

    Auto-WEKA is an automated machine learning system based on Weka by Chris Thornton, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown. [1] An extended version was published as Auto-WEKA 2.0. [2] Auto-WEKA was named the first prominent AutoML system in a neutral comparison study. [3] It received the test-of-time award of the SIGKDD conference ...

  5. Model selection - Wikipedia

    en.wikipedia.org/wiki/Model_selection

    Model selection may also refer to the problem of selecting a few representative models from a large set of computational models for the purpose of decision making or optimization under uncertainty. [2] In machine learning, algorithmic approaches to model selection include feature selection, hyperparameter optimization, and statistical learning ...

  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. Learning rate - Wikipedia

    en.wikipedia.org/wiki/Learning_rate

    In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. [1]

  8. AOL Mail

    mail.aol.com

    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  9. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    A machine learning model is a type of ... Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization. Genetic ...