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1. |
Record Nr. |
UNINA9910431345103321 |
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Autore |
Thorpe Holly |
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Titolo |
Feminist New Materialisms, Sport and Fitness : A Lively Entanglement / / by Holly Thorpe, Julie Brice, Marianne Clark |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Palgrave Macmillan, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XV, 268 p. 3 illus. in color.) |
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Collana |
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New Femininities in Digital, Physical and Sporting Cultures, , 2522-0349 |
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Disciplina |
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Soggetti |
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Sex |
Sports - Sociological aspects |
Social sciences - Philosophy |
Gender Studies |
Sport Sociology |
Social Theory |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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1. A Lively Introduction: New Materialisms, Feminisms, Moving Bodies -- 2. New Materialist Methods and the Research Process -- 3. Sporting Matter and Living with Objects of Fitness -- 4. Digital Intimacies, Assemblages, and Fit Femininities -- 5. The Biocultural Possibilities of Sportswomen's Health -- 6. Apparatus and the Boundaries of Transdisciplinary Research -- 7. Feminist Ethics, the Environment, and Vital Respondings. . |
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Sommario/riassunto |
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This book offers the first critical examination of the contributions of feminist new materialist thought to the study of sport, fitness, and physical culture. Bringing feminist new materialist theory into a lively dialogue with sport studies, it highlights the possibilities and challenges of engaging with posthumanist and new materialist theories. With empirical examples and pedagogical offerings woven throughout, the book makes complex new materialist concepts and theories highly accessible. It vividly illustrates sporting matter as lively, vital, and |
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agentic. Engaging specifically with the methodological, theoretical, ethical and political challenges of feminist new materialisms, it elaborates understandings of moving bodies and their entanglements with human, non-human, technological, biological, cultural, and environmental forces in contemporary society. This book extends humanist, representationalist, and discursiveapproaches that have characterized the landscape of critical research on active bodies, and invites new imaginings and articulations for sport and moving bodies in uncertain times and unknown futures. |
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2. |
Record Nr. |
UNINA9910741167703321 |
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Autore |
Zhao Haitao |
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Titolo |
Feature Learning and Understanding : Algorithms and Applications / / by Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XIV, 291 p. 126 illus., 109 illus. in color.) |
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Collana |
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Information Fusion and Data Science, , 2510-1528 |
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Disciplina |
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Soggetti |
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Sociophysics |
Econophysics |
Machine learning |
Computational intelligence |
Pattern perception |
Signal processing |
Image processing |
Speech processing systems |
Optical data processing |
Data-driven Science, Modeling and Theory Building |
Machine Learning |
Computational Intelligence |
Pattern Recognition |
Signal, Image and Speech Processing |
Image Processing and Computer Vision |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter1. A Gentle Introduction to Feature Learning -- Chapter2. Latent Semantic Feature Learning -- Chapter3. Principal Component Analysis -- Chapter4. Local-Geometrical-Structure-based Feature Learning -- Chapter5. Linear Discriminant Analysis -- Chapter6. Kernel-based nonlinear feature learning -- Chapter7. Sparse feature learning -- Chapter8. Low rank feature learning -- Chapter9. Tensor-based Feature Learning -- Chapter10. Neural-network-based Feature Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network. |
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Sommario/riassunto |
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This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence. |
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