This book presents a self-contained introduction to the theory of minisum hyperspheres. This specialized research area within the larger field of geometric optimization is full of interesting and open problems. This work provides an overview of the history of minisum hyperspheres as well as describes the best techniques for developing and solving minisum hypersphere problems. Various related areas of geometric and nonlinear optimization are also discussed. As the first publication devoted to this area of research, this work will be of great interest to graduate-level researchers studying minisum hypersphere problems as well as mathematicians interested geometric optimization.

A detailed description of what the fourth dimension would be like.

Hypersphere, written by Anonymous with the help of the 4chan board /lit/ (of The Legacy of Totalitarianism in a Tundra fame) is an epic tale spanning over 700 pages. A postmodern collaborative writing effort containing Slavoj Zizek erotica, top secret Donald Trump emails, poetry, repair instructions for future cars, a history of bottles in the Ottoman empire; actually, it contains everything since it takes place in the Hypersphere, and the Hypersphere is a big place; really big in fact.

This book constitutes the refereed proceedings of the 25th Symposium of the German Association for Pattern Recognition, DAGM 2003, held in Magdeburg, Germany in September 2003. The 74 revised papers presented were carefully reviewed and selected from more than 140 submissions. The papers address all current issues in pattern recognition and are organized in sections on image analyses, callibration and 3D shape, recognition, motion, biomedical applications, and applications.

This two-volume set, LNAI 9077 + 9078, constitutes the refereed proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2015, held in Ho Chi Minh City, Vietnam, in May 2015. The proceedings contain 117 paper carefully reviewed and selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional and sequential data; entity resolution and topic-modeling; itemset and high-performance data mining; and recommendations.

This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.