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Introduction. In general, a neural model can be characterized by 4 parameters: size of the model, size of the training dataset, cost of training, performance after training. Each of these four variables can be precisely defined into a real number, and they are empirically found to be related by simple statistical laws, called "scaling laws ...
In machine learning, a hyperparameter is a parameter, such as the learning rate or choice of optimizer, which specifies details of the learning process, hence the name hyper parameter. This is in contrast to parameters which determine the model itself. Hyperparameters can be classified as model hyperparameters, that typically cannot be inferred ...
Machine learningand data mining. Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.
Tensor (machine learning) Tensor informally refers in machine learning to two different concepts that organize and represent data. Data may be organized in a multidimensional array ( M -way array) that is informally referred to as a "data tensor"; however in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain ...
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. The method was invented by John Platt in the context of support vector machines , [1] replacing an earlier method by Vapnik , but can be applied to other classification models. [2]
Limited-memory BFGS ( L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. [1] It is a popular algorithm for parameter estimation in machine learning. [2] [3] The algorithm's target problem is ...
The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. [1] In 1959, Arthur Samuel defined machine learning as a "field of ...
Machine learningand data mining. In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance. [1] It is used in supervised learning and a family of machine learning algorithms that convert weak learners to strong ones. [2]