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Machine learning

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The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

Computer animation. --- Machine learning.

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Theoretical and practical aspects of machine learning (ML) algorithms and systems, ML systems involving applications in medicine, biology, industry, manufacturing, security, education, virtual environments, game playing, problem solving, and energy.

Data mining --- Machine learning

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Signal processing --- Machine learning --- Digital techniques

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The acceptance of reason with uncertainty can help learners successfully manage their occupations and lives during the accelerations prominent in the 21st century. As William Ayers states: “Pritscher tilts his lance at the petrified orthodoxy we call teaching and learning, inviting us on a wild journey into the heart of education.” The book elaborates on David Geoffrey Smith’s question: “Why does so much educational ‘research’ today seem so unenlightening, repetitive and incapable of moving beyond itself? The answer must be because it is ‘paradigmatically stuck’, and cannot see beyond the parameters of its current imaginal space.” The book offers help to go beyond the current imaginal space through what is called kaplearning. Kaplearning can help the reader to defamiliarize the common by facilitating “letting go”. Pritscher takes an avant-garde approach to learning, pushing the boundaries of the long accepted norm “certainty and order” and modernizing education by trading the old “optimal way” with a new skill to “reason with uncertainty”. This resilience to ambiguity is precisely where human intelligence has full advantage over machine intelligence. Pritscher’s book is impressive and remarkably well-timed, as recent articles in Nature show that online game players can make surprising breakthroughs in science with a well-chosen confluence of effective sources and a bit of creativity with protein folding. Citizen science has led to solutions that scientists and computer simulators have struggled for years, proving that even with little or no scientific training, knowing what to ignore can invite innovating ways to think and execute. Pritscher’s clear and wise insight will definitely serve as an inspiration for the next generation of educators, and prepare the necessary skills for young learners to successfully compete in the future. - Sandra Okita - Department of Math, Science and Technology, Teachers College, Columbia University.

Teaching --- Teaching --- machine learning --- onderwijs --- onderwijs --- creativiteit