Series: Unsupervised and Semi-Supervised Learning

Sampling Techniques for Supervised or Unsupervised Tasks

Deep Biometrics

Feature and Dimensionality Reduction for Clustering With Deep Learning

Sampling Techniques for Supervised or Unsupervised Tasks

Innovations in Computational Logistics and Supply Chain Analytics

Advances in Computational Logistics and Supply Chain Analytics

Partitional Clustering Via Nonsmooth Optimization

Deep Biometrics

Mixture Models and Applications

Unsupervised Feature Extraction Applied to Bioinformatics

Unsupervised Feature Extraction Applied to Bioinformatics

Hidden Markov Models and Applications

Supervised and Unsupervised Learning for Data Science

Machine Learning and Data Analytics for Solving Business Problems

Feature and Dimensionality Reduction for Clustering with Deep Learning

Unsupervised Feature Extraction Applied to Bioinformatics

Clustering Methods for Big Data Analytics

Hidden Markov Models and Applications