Data Research Papers

Book Review: Pattern Recognition and Machine Learning

This PDF file contains the editorial “Pattern Recognition and Machine Learning” for JEI Vol. 16 Issue 04...

Foundations of Machine Learning

Foundations of Machine LearningSoon we will embark on a theoretical study of AdaBoost in order to understand its properties, particularly its ability as a learning algorithm to generalize, that is, to make accurate predictions on data not s...

A Review on Fairness in Machine Learning

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college ad...

Python 3 for Machine Learning

This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fun...

MATLAB Machine Learning

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It prov...

Machine Learning: ECML-98

Introduction to Machine Learning

A survey on machine learning for data fusion

Automated Machine Learning

This open access book gives the first comprehensive overview of general methods in Automatic Machine Learning, AutoML, collects descriptions of existing AutoML systems based on these methods, and discusses the first international challenge ...

Adversarial machine learning

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a...

Introduction to machine learning

The machine learning approach is to train an algorithm using a dataset for which the people do know the outcome, and then apply this algorithm in the future to make a prediction when they don't know the outcome. In this chapter, the authors...

Encyclopedia of Machine Learning and Data Mining

Supervised Machine Learning: A Brief Primer

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the...

Introduction to Machine Learning

mlr: Machine Learning in R

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensiti...

Machine Learning Mastery

This chapter delves into the core principles of machine learning, offering practical insights for effective data processing. From foundational concepts to advanced techniques, the narrative unfolds as a comprehensive guide for harnessing th...

An Introduction to Machine Learning

Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning tech...

Machine Learning, Neural and Statistical Classification

Survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning....