Search results
Results from the WOW.Com Content Network
Principal component analysis (PCA) is a technique that transforms data to a new coordinate system such that the directions capturing the largest variation in the data can be easily identified. PCA has applications in exploratory data analysis, visualization and data preprocessing, and is related to factor analysis and canonical correlation analysis.
Cluster analysis is the task of grouping a set of objects based on their similarity or distance. Learn about different types of cluster models, algorithms and applications in various fields, such as machine learning, data mining and pattern recognition.
Learn how to test for the convergence, divergence, or absolute convergence of an infinite series using various criteria and examples. Find out how to apply the limit, ratio, root, integral, p-series, direct comparison, limit comparison, Cauchy condensation, Abel's, alternating series, Dirichlet's, Cauchy's, Stolz–Cesàro, Weierstrass M-test, and other tests.
3. Automated data analysis for visual reports that help identify patterns. AI technology in data analysis tools such as IBM Watson or Google's Looker Studio can help you make sense of your ...
Convergence rate. Precision. Robustness. General performance. Here some test functions are presented with the aim of giving an idea about the different situations that optimization algorithms have to face when coping with these kinds of problems. In the first part, some objective functions for single-objective optimization cases are presented.
In mathematics, the ratio test is a test (or "criterion") for the convergence of a series =, where each term is a real or complex number and a n is nonzero when n is large. The test was first published by Jean le Rond d'Alembert and is sometimes known as d'Alembert's ratio test or as the Cauchy ratio test.
The Mandelbrot set is a complex fractal curve that exhibits great complexity and aesthetic appeal. It is defined by a simple iteration of a quadratic map and has many properties and applications in mathematics and computer graphics.
Exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. Learn about the history, objectives, techniques and tools of EDA, and how it differs from confirmatory data analysis.