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The program evaluation and review technique (PERT) is a statistical tool used in project management, which was designed to analyze and represent the tasks involved in completing a given project. PERT was originally developed by Charles E. Clark for the United States Navy in 1958; it is commonly used in conjunction with the Critical Path Method ...
General numerical computing package with many extension modules. Syntax mostly compatible with MATLAB IGOR Pro: WaveMetrics 1986 1988 8.00 May 22, 2018: $995 (commercial) $225 upgrade, $499 (academic) $175 upgrade, $85 (student) Proprietary: interactive graphics, programmable, 2D/3D, used for science and engineering, large data sets. imc FAMOS
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. Although MATLAB is intended primarily for numeric computing, an optional toolbox uses the MuPAD symbolic engine allowing access to symbolic computing abilities.
Graphical Evaluation and Review Technique (GERT) is a network analysis technique used in project management that allows probabilistic treatment both network logic and estimation of activity duration. The technique was first described in 1966 by Dr. Alan B. Pritsker of Purdue University and WW Happ.
The CIPP evaluation model is a program evaluation model which was developed by Daniel Stufflebeam and colleagues in the 1960s. CIPP is an acronym for context, input, process and product. CIPP is a decision-focused approach to evaluation and emphasizes the systematic provision of information for program management and operation. [1]
On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection
Maximum likelihood estimation (MLE) is a standard statistical tool for finding parameter values (e.g. the unmixing matrix ) that provide the best fit of some data (e.g., the extracted signals ) to a given a model (e.g., the assumed joint probability density function (pdf) of source signals).