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Sparse Estimation with Math and R
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can ...
Statistical Learning with Math and R
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mat...
PRICAI 2002: Trends in Artificial Intelligence
This book constitutes the refereed proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002, held in Tokyo, Japan in August 2002. The 57 revised full papers presented together with 5 invited contributions and 26 posters were carefully reviewed and selected from 161 submissions. The papers are organized in topical sections on logic and AI foundations, representation and reasoning of actions, constraint satisfaction, foundations of agents, foundations of learning, reinforcement learning, knowledge acquisition and management, data mining and knowledge discovery, neural network learning, learning for robots, multi-agent applications, document analysis, Web intelligence, bioinformatics, intelligent learning environments, face recognition, and multimedia and emotion.
Statistical Learning with Math and Python
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapte...
Where Happiness Dwells
The Dane-zaa people have lived in BC’s Peace River area for thousands of years. Elders documented the people’s history and worldview in oral narratives and passed them on through storytelling. Language loss, however, threatens to break the bonds of knowledge transmission. At the request of the Doig River First Nation, anthropologists Robin and Jillian Ridington present a history of the Dane-zaa people based on oral histories collected over a half century of fieldwork. These powerful stories span the full length of history, from the story of creation to the fur trade, from the arrival of missionaries to modern land claim cases. Elders document key events as they explain the very nature of the universe. The Dane-zaa were one of the last nations to experience the effects of colonialism. Where Happiness Dwells not only preserves their traditional knowledge for future generations, it also tells the inspiring story of how they learned to succeed in the modern world.
Advances in Cryptology – EUROCRYPT '99
This book constitutes the refereed proceedings of the International Conference on the Theory and Application of Cryptographic Techniques, EUROCRYPT '99, held in Prague, Czech Republic in May 1999. The 32 revised full papers presented were carefully selected during highly competitive reviewing process. The book is divided in topical sections on cryptanalysis, hash functions, foundations, public key cryptosystems, watermarking and fingerprinting, elliptic curves, new schemes, block ciphers, distributed cryptography, tools from related areas, and broadcast and multicast.
9th Circuit Update
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Special Publication
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