This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications.  Meer

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications.  Meer

This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool.  Meer

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others.  Meer

This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually.  Meer

This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages.  Meer

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications.  Meer

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications.  Meer

This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages.  Meer

This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool.  Meer

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it “Deep Biometrics”. The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on.  Meer

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it “Deep Biometrics”. The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on.  Meer

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems.  Meer

This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes.  Meer

This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question.  Meer

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.  Meer

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.  Meer

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.  Meer

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms.  Meer

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms.  Meer