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Machine Learning With Matlab. Unsupervised Learning Techniques: Classification
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Machine Learning With Matlab. Unsupervised Learning Techniques: ClassificationНазвание: Machine Learning With Matlab. Unsupervised Learning Techniques: Classification
Автор: Cesar Perez Lopez
Издательство: Lulu.com
Год: 2020
Страниц: 341
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

The availability of large volumes of data and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics that includes Multivariate Data Analysis (MDA), Machine Learning, Data Mining and other Business Intelligence techniques. Machine Learning (ML) uses two types of techniques: Supervised Learning techniques (predictive techniques), which trains a model on known input and output data so that it can predict future outputs, and Supervised Learning techniques (descriptive techniques), which finds hidden patterns or intrinsic structures in input data. Unsupervised learning techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses.

Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops classification unsupervised learning techniques.

MATLAB provides tools to help you try out a variety of machine learning models and choose the best. Some Machine Learning tasks are made easier by using apps, and others use command-line features.

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