Data Analysis, Machine Learning and Knowledge Discovery - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Analysis, Machine Learning and Knowledge Discovery write by Myra Spiliopoulou. This book was released on 2013-11-26. Data Analysis, Machine Learning and Knowledge Discovery available in PDF, EPUB and Kindle. Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012.
Machine Learning and Knowledge Discovery in Databases
Machine Learning and Knowledge Discovery in Databases - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning and Knowledge Discovery in Databases write by Michelangelo Ceci. This book was released on 2017-12-29. Machine Learning and Knowledge Discovery in Databases available in PDF, EPUB and Kindle. The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.
Machine Learning for Knowledge Discovery with R
Machine Learning for Knowledge Discovery with R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Knowledge Discovery with R write by Kao-Tai Tsai. This book was released on 2021-09-14. Machine Learning for Knowledge Discovery with R available in PDF, EPUB and Kindle. Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein. Key Features: Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies. Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations. Written by statistical data analysis practitioner for practitioners. The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.
Data Mining and Machine Learning
Data Mining and Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Mining and Machine Learning write by Mohammed J. Zaki. This book was released on 2020-01-30. Data Mining and Machine Learning available in PDF, EPUB and Kindle. New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.
The Essentials of Data Science: Knowledge Discovery Using R
The Essentials of Data Science: Knowledge Discovery Using R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Essentials of Data Science: Knowledge Discovery Using R write by Graham J. Williams. This book was released on 2017-07-28. The Essentials of Data Science: Knowledge Discovery Using R available in PDF, EPUB and Kindle. The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
