Springer Supervised and Unsupervised Learning for Data Science

Supervised and Unsupervised Learning for Data Science

Springer

Advances in Data Clustering: Theory and Applications

Springer

Supervised and Unsupervised Statistical Data Analysis (Studies in Classification, Data Analysis, and Knowledge Organization)

Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)

Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach (Unsupervised and Semi-Supervised Learning)

Springer

Springer Unsupervised Feature Extraction in Bioinformatics Book

Springer

Partitional Clustering via Nonsmooth Optimization: Clustering via Optimization (Unsupervised and Semi-Supervised Learning)

Springer

Partitional Clustering via Nonsmooth Optimization: Clustering via Optimization (Unsupervised and Semi-Supervised Learning)

Springer

Statistical Learning and Data Science (Computer Science and Data Analysis)

CRC Press

Statistical Learning and Data Science (Chapman & Hall/CRC Computer Science & Data Analysis)

CRC Press

Springer - Hidden Markov Models and Applications Book

Springer

Unsupervised Domain Adaptation: Recent Advances and Future Perspectives (Machine Learning: Foundations, Methodologies, and Applications)

Data Classification and Incremental Clustering in Data Mining and Machine Learning (EAI/Springer Innovations in Communication and Computing)

Springer

Unsupervised Domain Adaptation: Recent Advances and Future Perspectives (Machine Learning: Foundations, Methodologies, and Applications)

Springer

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

Springer

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

Springer

Machine Learning Methods

Springer

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)

Springer

Data Science and Big Data Computing: Frameworks and Methodologies

Springer

Recent Trends and Future Challenges in Learning from Data (Studies in Classification, Data Analysis, and Knowledge Organization)

Springer

Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)

Springer

Pattern Recognition and Classification: An Introduction

Springer

Springer Machine Learning and Data Mining for Computer Security

Springer

Data Science for Healthcare: Methodologies and Applications

Springer