Being Supervised, De Haan, Erik , Regouin, Willemine
Новое издание
Автор: de Haan, Erik Название: Being Supervised ISBN: 1032382228 ISBN-13(EAN): 9781032382227 Издательство: Taylor&Francis Цена: 132710 T Наличие на складе: Нет в наличии.
Автор: Langs, Robert Название: Doing Supervision and Being Supervised ISBN: 0367324156 ISBN-13(EAN): 9780367324155 Издательство: Taylor&Francis Рейтинг: Цена: 137810.00 T Наличие на складе: Невозможна поставка. Описание: There is always a lively interest in the supervisory process and its explication. Courses in supervision abound and the critical role of supervision in becoming a psychotherapist is widely acknowledged. It is for this reason that this book aims to present the essentials of supervision, establish validated principles of teaching and learning.
Автор: Jones Princess Название: Supervised ISBN: 098303236X ISBN-13(EAN): 9780983032366 Издательство: Неизвестно Рейтинг: Цена: 13790.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Alex Graves Название: Supervised Sequence Labelling with Recurrent Neural Networks ISBN: 3642432182 ISBN-13(EAN): 9783642432187 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book offers a complete framework for classifying and transcribing sequential data with recurrent neural networks. It uses state-of-the-art results in speech and handwriting recognition to show the framework in action.
Автор: Janosik Steven M., Cooper Diane L., Saunders Sue A Название: Learning Through Supervised Practice in Student Affairs ISBN: 0415534348 ISBN-13(EAN): 9780415534345 Издательство: Taylor&Francis Рейтинг: Цена: 47970.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Written for use in a student affairs practicum, this text explores the theories that foster learning while exercises, reflection activities, and case studies illuminate the skills that students must develop to become successful practitioners.
Автор: Oleg Okun Название: Applications of Supervised and Unsupervised Ensemble Methods ISBN: 3642039987 ISBN-13(EAN): 9783642039980 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Expanding upon presentations at last year`s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.
Автор: Oleg Okun Название: Applications of Supervised and Unsupervised Ensemble Methods ISBN: 3642260772 ISBN-13(EAN): 9783642260773 Издательство: Springer Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book contains the extended papers presented at the 2nd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA)heldon21-22July,2008inPatras, Greece, inconjunctionwiththe 18thEuropeanConferenceon Arti?cial Intelligence(ECAI 2008). This wo- shop was a successor of the smaller event held in 2007 in conjunction with 3rd Iberian Conference on Pattern Recognition and Image Analysis, Girona, Spain. The success of that event as well as the publication of workshop - pers in the edited book "Supervised and Unsupervised Ensemble Methods and their Applications", published by Springer-Verlag in Studies in Com- tational Intelligence Series in volume 126, encouraged us to continue a good tradition. The scope of both SUEMA workshops (hence, the book as well) is the application of theoretical ideas in the ?eld of ensembles of classi?cation and clusteringalgorithmstoreal/lifeproblemsinscienceandindustry. Ensembles, which represent a number of algorithms whose class or cluster membership predictions are combined together to produce a single outcome value, have alreadyprovedto be a viable alternativeto a single best algorithmin various practical tasks under di?erent scenarios, from bioinformatics to biometrics, from medicine to network security. The ensemble approach is caused to life by the famous "no free lunch" theorem, stating that there is no absolutely best algorithm to solve all problems. Although ensembles cannot be cons- ered as absolute remedy of a single algorithm de?ciency, it is widely believed thatensemblesprovideabetteranswerto"nofreelunch"theoremthanas- glebestalgorithm. Statistical, algorithmical, representational, computational and practical reasons can explain the success of ensemble methods.
Автор: Oleg Okun Название: Supervised and Unsupervised Ensemble Methods and their Applications ISBN: 3642097766 ISBN-13(EAN): 9783642097768 Издательство: Springer Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Ensembles of Classification Methods and Their Applications.- Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.- Erratum.
Автор: Oleg Okun Название: Supervised and Unsupervised Ensemble Methods and their Applications ISBN: 3540789804 ISBN-13(EAN): 9783540789802 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Ensembles of Classification Methods and Their Applications.- Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.- Erratum.
