This volume constitutes the refereed proceedings of the Eighth International Conference on Cognition and Recognition, ICCR 2021, held in Mandya, India, in December 2021.The 24 full papers and 9 short papers presented were carefully reviewed and selected from 150 submissions. The ICCR conference aims to bring together leading academic Scientists, Researchers and Research scholars to exchange and share their experiences and research results on all aspects of Computer Vision, Image Processing Machine Learning and Deep Learning Technologies.

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021.The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021.The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021.The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.

This book describes the need to develop classifiers for multi-class problems which can provide better accuracy. SVMs deliver state-of-the-art performance in real-world multi-class classification applications such as text categorization, hand-written character recognition, image classification, biosequences analysis and intrusion detection. Their first beginning in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now recognized Support Vector Machines as one of the standard tools for machine learning and data mining. The main goal of this book is to develop SVM classifiers for multi-class problems which can provide better accuracy. Students will find the book both stimulating and accessible, while researchers will be guided smoothly through the material required for a good grasp of the theory and application of these classifiers. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is meticulous and thorough. Pointers to relevant literature ensure that it forms an ideal starting point for further study.