Advances in Computational Logistics and Supply Chain Analytics (Unsupervised and Semi-Supervised Learning)

✍ Scribed by Ibraheem Alharbi (editor), Chiheb-Eddine Ben Ncir (editor), Bader Alyoubi (editor), Hajer Ben-Romdhane (editor)

Publisher: Springer
Year: 2024
Tongue: English
Leaves: 210
Edition: 2024
Category: Library

⬇ Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis

This book provides advances in computational logistics and supply chain analytics. The authors include innovative data-driven and learning-based approaches, methods, algorithms, techniques, and tools that have been designed or applied to create and implement a successful logistics and supply chain management process. This book highlights the state of the art and challenges related to the design and the application of computational methods to solve logistic and supply chain management problems. The authors present recent computational logistic methods and supply chain analytics techniques designed and applied to support managers in improving such complex processes. This book broadly covers recent computational methods and techniques applied to ensure continuous improvement of transport, logistic, and supply chain management processes. Readers can rapidly explore these new methods and their applications to solve such complex problems.

📜 SIMILAR VOLUMES

Advances in Computational Logistics and

📁 Advances in Computational Logistics and Supply Chain Analytics (Unsupervised and Semi-Supervised Learning)

✍ Ibraheem Alharbi (editor), Chiheb-Eddine Ben Ncir (editor), Bader Alyoubi (edito 📂 Library 📅 2024 🏛 Springer 🌐 English

This book provides advances in computational logistics and supply chain analytics. The authors include innovative data-driven and learning-based approaches, methods, algorithms, techniques, and tools that have been designed or applied to create and implement a successful logistics and suppl

Semi-Supervised and Unsupervised Machine

📁 Semi-Supervised and Unsupervised Machine Learning: Novel Strategies

✍ Amparo Albalate, Wolfgang Minker(auth.) 📂 Library 📅 2010 🏛 Wiley-ISTE 🌐 English

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining

Machine Learning Foundations: Supervised

📁 Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning

✍ Taeho Jo 📂 Library 📅 2021 🏛 Springer 🌐 English

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental mat

Hidden Markov Models and Applications (U

📁 Hidden Markov Models and Applications (Unsupervised and Semi-Supervised Learning)

✍ Nizar Bouguila (editor), Wentao Fan (editor), Manar Amayri (editor) 📂 Library 📅 2022 🏛 Springer 🌐 English

This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a sp

Kernel Based Algorithms for Mining Huge

📁 Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning

✍ Te-Ming Huang, Vojislav Kecman, Ivica Kopriva 📂 Library 📅 2006 🏛 Springer 🌐 English

This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs

Kernel Based Algorithms for Mining Huge

📁 Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning

✍ Te-Ming Huang, Vojislav Kecman, Ivica Kopriva (auth.) 📂 Library 📅 2006 🏛 Springer 🌐 English

"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an