Resve A. Saleh – författare

This work addresses the problems of simulating entire mixed analog/digital systems in the time-domain. A complete hierarchy of modelling and simulation methods for analog and digital circuits is described. It also provides a chronology of the research in the field of mixed-mode simulation and analog multilevel simulation over the last ten to 15 years. In addition, it provides enough information to the reader so that a prototype mixed-mode simulator could be developed using the algorithms in this book. The text can also be used as documentation for the SPLICE family of mixed-mode programs as they are based on the algorithms and techniques described in this book.

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students. Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: Development of rank theory and application of shrinkage and selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning

An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problemsFundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

Mixed-Mode Simulation and Analog Multilevel Simulation addresses the problems of simulating entire mixed analog/digital systems in the time-domain. A complete hierarchy of modeling and simulation methods for analog and digital circuits is described. Mixed-Mode Simulation and Analog Multilevel Simulation also provides a chronology of the research in the field of mixed-mode simulation and analog multilevel simulation over the last ten to fifteen years. In addition, it provides enough information to the reader so that a prototype mixed-mode simulator could be developed using the algorithms in this book. Mixed-Mode Simulation and Analog Multilevel Simulation can also be used as documentation for the SPLICE family of mixed-mode programs as they are based on the algorithms and techniques described in this book.