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In statistical process control (SPC), the ¯ and R chart is a type of scheme, popularly known as control chart, used to monitor the mean and range of a normally distributed variables simultaneously, when samples are collected at regular intervals from a business or industrial process. [1]
There are distribution-free control charts for both Phase-I analysis and Phase-II monitoring. One of the most notable distribution-free control charts for Phase-I analysis is RS/P chart proposed by G. Capizzi and G. Masaratto. RS/P charts separately monitor location and scale parameters of a univariate process using two separate charts.
JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
The optimization is only based on the control performance (cost function) as measured in the plant. Genetic programming is a powerful regression technique for this purpose. [5] Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using reinforcement learning. [6]
The generalized additive model for location, scale and shape (GAMLSS) is a semiparametric regression model in which a parametric statistical distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables. GAMLSS is a form of supervised machine learning.
Other machine learning algorithms such as neural network are provided in microsoftml, a separate package that is the Python version of MicrosoftML. [ 3 ] revoscalepy also contains functions designed to run machine learning algorithms in different compute contexts, including SQL Server, Apache Spark , and Hadoop .
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scikit-learn, a popular machine learning library in Python implements t-SNE with both exact solutions and the Barnes-Hut approximation. Tensorboard, the visualization kit associated with TensorFlow, also implements t-SNE (online version) The Julia package TSne implements t-SNE