Cover Image

Supervised learning : mathematical foundations and real-world applications /

"This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this re...

Full description

Bibliographic Details
Main Author:	Chakrabarty, Dalia (Author)
Format:	eBook
Language:	English
Published:	Boca Raton, FL : CRC Press, 2025.
Edition:	First edition.
Subjects:	Supervised learning (Machine learning) Supervised learning (Machine learning) > Mathematical models. Apprentissage supervisé (Intelligence artificielle) Apprentissage supervisé (Intelligence artificielle) > Modèles mathématiques.

Similar Items

Types of supervised machine learning.
Published: (2019)

What is supervised machine learning?.
Published: (2019)

Machine learning from weak supervision : an empirical risk minimization approach /
by: Sugiyama, Masashi, 1974-
Published: (2022)

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems : Prediction Models Exploiting Well-Log Information.
by: Wood, David A. (Petroleum engineer)
Published: (2025)

Learn about sentiment analysis with supervised learning in R with data from the Economic News Article Tone dataset (2016) /
by: Shi, Feng, active 2019
Published: (2019)

Supervised learning in remote sensing and geospatial science : theory and practice /
by: Maxwell, Aaron E., et al.
Published: (2025)

Learn about sentiment analysis with supervised learning in Python with data from the Economic News Article Tone dataset (2016) /
by: Shi, Feng
Published: (2019)

Semi-supervised learning /
Published: (2006)

Introduction to semi-supervised learning /
by: Zhu, Xiaojin, Ph. D.
Published: (2009)

Introduction to semi-supervised learning /
by: Zhu, Xiaojin, Ph. D.
Published: (2009)

Embracing the value of data in broadcasting : Channel 4.
Published: (2019)

The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations /
by: Hastie, Trevor
Published: (2001)

The elements of statistical learning : data mining, inference, and prediction /
by: Hastie, Trevor
Published: (2009)

The elements of statistical learning : data mining, inference, and prediction /
by: Hastie, Trevor
Published: (2009)

The elements of statistical learning : data mining, inference, and prediction /
by: Hastie, Trevor
Published: (2001)

Machine learning & predictive modelling for recommendations & insight : Mallzee.
Published: (2019)

Semi-supervised learning /
Published: (2006)

Explainable deep learning AI : methods and challenges /
Published: (2023)

Boosting : foundations and algorithms /
by: Schapire, Robert E.
Published: (2012)

Graph-based semi-supervised learning /
by: Subramanya, Amarnag, et al.
Published: (2014)

Multi-label dimensionality reduction /
by: Sun, Liang, et al.
Published: (2014)

Machine learning : proceedings of the ninth international workshop (ML92) /
Published: (1992)

MACHINE LEARNING FUNDAMENTALS concepts, models, and applications.
by: SAHAY, AMAR
Published: (2025)

Machine learning methods for planning /
Published: (1993)

The mathematics of generalization : the proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning /
Published: (1995)

MASTERING THE MINDS OF MACHINES a journey into deep learning and AI.
Published: (2025)

Machine learning : proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12, 1995 /
Published: (1995)

Machine learning : an artificial intelligence approach.
by: Bareiss, E. Ray
Published: (1990)

Empowering artificial intelligence through machine learning : new advances and applications /
Published: (2022)

Supervised machine learning : optimization framework and applications with SAS and R /
by: Kolosova, Tanya, et al.
Published: (2021)

Getting started with vector databases and AI embeddings.
Published: (2025)

Machine learning : an artificial intelligence approach /
Published: (1983)

Real-world machine learning projects with Scikit-Learn /
Published: (2018)

De-Mystifying Math & Stats for Machine Learning : Mastering the Fundamentals of Mathematics and Statistics for Machine Learning /
by: Kumar, Govind
Published: (2021)

Chu tan shen du xue xi : shi yong TensorFlow = TensorFlow for deep learning : from linear regression to reinforcement learning /
by: Ramsundar, Bharath, et al.
Published: (2018)

Deep learning techniques for biomedical and health informatics /
Published: (2020)

Learning to rank for information retrieval and natural language processing /
by: Li, Hang, 1965-
Published: (2011)

Federated learning for digital healthcare systems /
Published: (2024)

Supervision as transformation : a passion for learning /
Published: (2011)

Signal processing driven machine learning techniques for cardiovascular data processing /
Published: (2024)