Artificial Intelligence Design and Solution for Risk and Security by Archie Addo & Muthu Shanmugam

Author:Archie Addo & Muthu Shanmugam
Language: eng
Format: epub
Publisher: Business Expert Press
Published: 2020-03-12T12:17:38+00:00

Mixture Discriminant Analysis

The mixture discriminant analysis is a technique used to analyze the research data when the criterion or the dependent variable is categorical and when the predictor or the independent variable is an interval in nature. Discriminant analysis is a classification problem where two or more groups, clusters, or populations are known a priori, and one or more new observations are classified into one of the known populations based on the measured characteristics.

Quadratic Discriminant Analysis (QDA)

The QDA for nominal labels and numerical attributes determines which variables discriminate between two or more naturally occurring groups. This technique also determines whether the variables have a descriptive or a predictive objective. The QDA may also refer to qualitative data analysis when used in qualitative research. The QDA extension is used for quadruple discriminant archives. QDA is used in statistical classification or used as a quadratic classifier in ML.

Regularized Discriminant Analysis

Regularized discriminant analysis is a method that generalizes various class-conditional Gaussian classifiers, including linear discriminant analysis, QDA, and Gaussian Naive Bayes. The linear discriminant analysis method offers a continuum between these models by tuning two hyper-parameters that control the amount of regularization applied to the estimated covariance matrices. Linear discriminant analysis is the most commonly used classification method for movement intention decoding from myoelectric signals.

AI and ML Process Steps

The AI process steps are shown in Figure 2.10. It is important that you follow the steps carefully to understand the processes that are required (Shrestha et al. 2019).

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