Advances in Feature Selection for Data and Pattern Recognition, Urszula Sta?czyk; Beata Zielosko; Lakhmi C. Jain

Автор: Yongzhen Huang; Tieniu Tan
Название: Feature Coding for Image Representation and Recognition
ISBN: 3662449994 ISBN-13(EAN): 9783662449998
Издательство: Springer
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Цена: 46570.00 T
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Описание: This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline.

Автор: Urszula Sta?czyk; Beata Zielosko; Lakhmi C. Jain
Название: Advances in Feature Selection for Data and Pattern Recognition
ISBN: 3319675877 ISBN-13(EAN): 9783319675879
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of recent advances. The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions and new applications.

Автор: Pablo Duboue
Название: The Art of Feature Engineering: Essentials for Machine Learning
ISBN: 1108709389 ISBN-13(EAN): 9781108709385
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.

Автор: Michael Felsberg
Название: Probabilistic and Biologically Inspired Feature Representations
ISBN: 1681733668 ISBN-13(EAN): 9781681733661
Издательство: Mare Nostrum (Eurospan)
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Цена: 66530.00 T
Наличие на складе: Невозможна поставка.
Описание: pacote do Courseware consiste em duas publicacoes, VeriSMTM - Foundation Courseware e VeriSM - Foundation Study Guide. Este material de treinamento abrange o plano de estudos para a qualificacao da Fundacao VeriSM . O treinamento pode ser entregue em dois dias. Este material didatico e credenciado para preparar o aluno para a certificacao da VeriSM Foundation. O VeriSM Foundation consiste em duas partes: VeriSM Essentials e VeriSM Plus, cada uma cobrindo um dia de treinamento.Os alunos que ja possuem um certificado de Gerenciamento de Servicos (TI) podem se beneficiar do conhecimento que ja possuem. Eles sao o publico-alvo de apenas um treinamento do VeriSM Plus. Ao serem aprovados no exame VeriSM Plus, recebem o certificado VeriSM Foundation.Provedores de treinamento que desejam oferecer um treinamento de um dia sobre principios de gerenciamento de servicos podem decidir oferecer apenas o treinamento VeriSM Essentials. Os alunos que forem aprovados no exame VeriSM Essentials receberao o certificado VeriSM Essentials. Se eles passarem no exame VeriSM Plus mais tarde, receberao automaticamente o certificado VeriSM Foundation.O "courseware" abrange os seguintes topicos:A organizacao do servico (Essentials)Cultura de servico (Essentials)Pessoas e estrutura organizacional (Essentials)O modelo VeriSM (ambos)Praticas Progressivas (Plus)Tecnologias Inovadoras (Plus)O VeriSM e uma abordagem holistica e orientada aos negocios para o Gerenciamento de Servicos, que ajuda a entender o panorama crescente das melhores praticas e como integra-las para oferecer valor ao consumidor.E uma evolucao no pensamento em Gerenciamento de Servicos e oferece uma abordagem atualizada, incluindo as mais recentes praticas e desenvolvimentos tecnologicos, para ajudar as organizacoes a transformar seus negocios para a nova realidade da era digital.O VeriSM e um gerenciamento orientado a valor, evolutivo, responsivo e integrado.VeriSM e uma marca registrada e propriedade da IFDC, a Fundacao Internacional de Competencias Digitais.

Автор: Pramod Kumar Pisharady; Prahlad Vadakkepat; Loh Ai
Название: Computational Intelligence in Multi-Feature Visual Pattern Recognition
ISBN: 9811011710 ISBN-13(EAN): 9789811011719
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds.

Автор: Pramod Kumar Pisharady; Prahlad Vadakkepat; Loh Ai
Название: Computational Intelligence in Multi-Feature Visual Pattern Recognition
ISBN: 9812870555 ISBN-13(EAN): 9789812870551
Издательство: Springer
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Цена: 121890.00 T
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Описание: This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds.

Автор: Michael Felsberg
Название: Probabilistic and Biologically Inspired Feature Representations
ISBN: 1681730235 ISBN-13(EAN): 9781681730233
Издательство: Mare Nostrum (Eurospan)
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Цена: 46200.00 T
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Описание: Under the title Probabilistic and Biologically Inspired Feature Representations, this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife—they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.

Автор: Bol?n-Canedo
Название: Recent Advances in Ensembles for Feature Selection
ISBN: 331990079X ISBN-13(EAN): 9783319900797
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method.

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Автор: Ver?nica Bol?n-Canedo; Noelia S?nchez-Maro?o; Ampa
Название: Feature Selection for High-Dimensional Data
ISBN: 3319218573 ISBN-13(EAN): 9783319218571
Издательство: Springer
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Цена: 93160.00 T
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Описание: Introduction to High-Dimensionality.- Foundations of Feature Selection.- Experimental Framework.- Critical Review of Feature Selection Methods.- Application of Feature Selection to Real Problems.- Emerging Challenges.

Автор: Wan, Cen
Название: Hierarchical feature selection for knowledge discovery
ISBN: 3319979183 ISBN-13(EAN): 9783319979182
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.

Автор: Jyotismita Chaki; Nilanjan Dey
Название: Texture Feature Extraction Techniques for Image Recognition
ISBN: 9811508526 ISBN-13(EAN): 9789811508523
Издательство: Springer
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Цена: 51230.00 T
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Описание: The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based.