Автор: Marco Wiering; Martijn van Otterlo Название: Reinforcement Learning ISBN: 364244685X ISBN-13(EAN): 9783642446856 Издательство: Springer Рейтинг: Цена: 32651.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents up-to-date information on the main contemporary sub-fields of reinforcement learning, including partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations.
Автор: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642018815 ISBN-13(EAN): 9783642018817 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reinforcement Learning Background.- Related Work.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future Work.
Автор: Bruno Baruque Название: Fusion Methods for Unsupervised Learning Ensembles ISBN: 3642423280 ISBN-13(EAN): 9783642423284 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets.
Автор: Hastie Название: Statistical Learning with Sparsity ISBN: 1498712169 ISBN-13(EAN): 9781498712163 Издательство: Taylor&Francis Рейтинг: Цена: 16843.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Discover New Methods for Dealing with High-Dimensional Data
A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.
Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.
In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.
Описание: This book reviews and analyzes new implementation perspectives for intelligent adaptive learning and collaborative systems, enabled by advances in scripting languages, IMS LD, educational modeling languages and learning activity management systems.
Описание: Technology-enhanced systems and computer-aided tools that support scaffolding in collaborative learning are key goals in the education research sector. This book covers a number of approaches to fostering functional collaborative learning and working online.
Автор: Rafael A. Calvo; Sidney K. D`Mello Название: New Perspectives on Affect and Learning Technologies ISBN: 1461429935 ISBN-13(EAN): 9781461429937 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Affective Prospecting integrates theoretical perspectives on learning with recent research in affective computing. These new perspectives are based on new research on emotion, cognition, and motivation applied to learning environments, Multimodal Human Computer Interfaces, and more.
Автор: Alexander Mehler; Kai-Uwe K?hnberger; Henning Lobi Название: Modeling, Learning, and Processing of Text-Technological Data Structures ISBN: 3642269443 ISBN-13(EAN): 9783642269448 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Researchers in many disciplines have been concerned with modeling textual data in order to account for texts as the primary information unit of written communication.
Автор: Przemys?aw R??ewski; Emma Kusztina; Ryszard Tadeus Название: Intelligent Open Learning Systems ISBN: 3642270786 ISBN-13(EAN): 9783642270789 Издательство: Springer Рейтинг: Цена: 21661.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: IOLSs enhance traditional online teaching methods by applying artificial intelligence and cognitive science. This book moves from analyzing OLSs and the role of the teacher, to knowledge modeling and ways of transferring competence in the virtual laboratory.
Автор: Fatos Xhafa; Santi Caball?; Ajith Abraham; Thanasi Название: Computational Intelligence for Technology Enhanced Learning ISBN: 3642262503 ISBN-13(EAN): 9783642262500 Издательство: Springer Рейтинг: Цена: 19589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book records advances in using intelligent techniques for technology enhanced learning, and development of e-Learning applications based on such techniques and supported by technology. Covers adaptive learning and data mining techniques, among others.
Автор: Shi Yu; L?on-Charles Tranchevent; Bart Moor; Yves Название: Kernel-based Data Fusion for Machine Learning ISBN: 3642267513 ISBN-13(EAN): 9783642267512 Издательство: Springer Рейтинг: Цена: 19589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data fusion problems arise in many different fields. This book provides a specific introduction to solve data fusion problems using support vector machines. The reader will require a good knowledge of data mining, machine learning and linear algebra.
Автор: Norbert Jankowski; W?odzis?aw Duch; Krzysztof Gr?b Название: Meta-Learning in Computational Intelligence ISBN: 3642268587 ISBN-13(EAN): 9783642268588 Издательство: Springer Рейтинг: Цена: 30606.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book defines and discusses new theoretical and practical trends in meta-learning, which shifts the focus of the field of computational intelligence (CI) from individual learning algorithms to the higher level of learning how to learn.
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