This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of…
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning…
This original and interdisciplinary volume explores the contemporary semiotic dimensions of the face from both scientific and sociocultural perspectives, putting forward several traditions, aspects, and signs of the human utopia of creating a hybrid…
This open access book offers a comprehensive and thorough introduction to almost all aspects of metalearning and automated machine learning (AutoML), covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization,…
In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data…
The legal domain distinguishes between different types of data and attaches a different level of protection to each of them. Thus, non-personal data are left largely unregulated, while privacy and data protection rules apply to personal data or…
This open access book systematically investigates the topic of entity alignment, which aims to detect equivalent entities that are located in different knowledge graphs. Entity alignment represents an essential step in enhancing the quality of…
“Introduction to Data Science and Machine Learning” has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application…