Azala

Statistical Machine Learning for Engineering with Applications

This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as...

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Xehetasun bibliografikoak
Erakunde egilea:	SpringerLink (Online service)
Beste egile batzuk:	Franke, Jürgen (Argitaratzailea), Schöbel, Anita (Argitaratzailea)
Formatua:	Baliabide elektronikoa eBook
Hizkuntza:	ingelesa
Argitaratua:	Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Edizioa:	1st ed. 2024.
Saila:	Lecture Notes in Statistics, 227
Gaiak:	Statistics . Machine learning. Statistical Theory and Methods. Machine Learning. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Sarrera elektronikoa:	https://doi.org/10.1007/978-3-031-66253-9
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Antzeko izenburuak

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Machine Learning in Bioinformatics An introduction to machine learning methods and their applications to problems in bioinformatics
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Argitaratua: (2009)

Regression Graph Models for Categorical Data Parameterization and Inference /
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Machine Learning with R : Learn how to Use R to Apply Powerful Machine Learning Methods and Gain an Insight Into Real-world Applications /
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Argitaratua: (2013)

Machine Learning with R Learn how to Use R to Apply Powerful Machine Learning Methods and Gain an Insight Into Real-world Applications
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Argitaratua: (2013)

Machine Learning using Python /
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Argitaratua: (2022)

Machine Learning using Python
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Argitaratua: (2022)

Robust and Multivariate Statistical Methods Festschrift in Honor of David E. Tyler /
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Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications Selected Contributions from SimStat 2019 and Invited Papers /
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New Trends in Functional Statistics and Related Fields
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Bayesian Nonparametric Statistics École d’Été de Probabilités de Saint-Flour LI - 2023 /
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Statistical Optimal Transport École d'Été de Probabilités de Saint-Flour XLIX – 2019 /
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Data Driven Model Learning for Engineers With Applications to Univariate Time Series /
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Sharp Inequalities for Ordered Random Variables in Statistics and Reliability Volume I: Standard Order Statistics /
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Principles of Machine Learning The Three Perspectives /
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Developments in Statistical Modelling
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Pattern recognition and machine learning /
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Argitaratua: (2006)

Pattern recognition and machine learning
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Minimum Gamma-Divergence for Regression and Classification Problems
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Applied Extreme Value Statistics With a Special Focus on the ACER Method /
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