Springer Machine Learning: Theoretical Foundations & Applications

Expand your knowledge of artificial intelligence with this comprehensive collection from Springer. This edited volume brings together expert insights presented at the 10th industry symposium held during the 16th edition of ICDCIT in January 2020. It serves as a vital resource for professionals and students looking to understand both the mathematical foundations and the real-world implementations of modern machine learning techniques. The text explores a wide range of specialized subjects, including statistical learning, neural network learning, and knowledge acquisition. Readers will find detailed discussions on machine learning for web navigation, mining, and information retrieval. Additionally, the book covers advanced topics such as text and multimedia mining, mobile data mining, and the development of distributed and parallel learning algorithms. Whether you are studying knowledge intensive learning or exploring new applications in big data, this book provides a structured look at the current state of the field.