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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 ...
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 ...
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).
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License.It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".
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 ...
Neural architecture search (NAS) [1] [2] is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning.NAS has been used to design networks that are on par with or outperform hand-designed architectures.
Machine learning (ML) is a field of ... from methodological principles to theoretical tools, ... surrogate models in Bayesian optimization used to do hyperparameter ...
Multi-task Bayesian optimization is a modern model-based approach that leverages the concept of knowledge transfer to speed up the automatic hyperparameter optimization process of machine learning algorithms. [8] The method builds a multi-task Gaussian process model on the data originating from different searches progressing in tandem. [9]