Machine Learning with Noisy Labels 1st Edition

Machine Learning with Noisy Labels, 1st Edition by Gustavo Carneiro is a timely, authoritative guide for anyone tackling real-world data where labels are imperfect. Clear, compelling, and technically rigorous, this book cuts through theory to deliver practical strategies for building robust models in the presence of noisy annotations.

Start reading and you’ll immediately see why this text is essential: it frames the sources and types of label noise, explains their impact on modern algorithms, and introduces principled methods—from robust loss functions and noise modeling to sample selection and semi-supervised remedies. Each chapter blends mathematical insight with hands-on examples and empirical evaluation so practitioners, researchers, and advanced students can translate concepts into better-performing systems.

Whether you work in healthcare imaging, autonomous systems, natural language processing, or enterprise analytics, the techniques here help reduce error, improve generalization, and make models more trustworthy across diverse datasets and global deployment scenarios. The book also surveys current research directions and practical pitfalls, equipping teams in academia and industry across North America, Europe, Asia, and beyond to design experiments and interpret results under label uncertainty.

Readable without sacrificing depth, Machine Learning with Noisy Labels is both a reference and a roadmap for improving model reliability when perfect labels aren’t available. Add this indispensable resource by Gustavo Carneiro to your library to sharpen your expertise and drive better outcomes from noisy, real-world data. Order now to elevate your machine learning practice.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.