Контакты/Проезд  Доставка и Оплата Помощь/Возврат
История
  +7(495) 980-12-10
  пн-пт: 10-18 сб,вс: 11-18
  shop@logobook.ru
   
    Поиск книг                    Поиск по списку ISBN Расширенный поиск    
Найти
  Зарубежные издательства Российские издательства  
Авторы | Каталог книг | Издательства | Новинки | Учебная литература | Акции | Хиты | |
 

Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches, Lepore


Варианты приобретения
Цена: 6986.00р.
Кол-во:
Наличие: Поставка под заказ.  Есть в наличии на складе поставщика.
Склад Америка: Есть  
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
При условии наличия книги у поставщика.

Добавить в корзину
в Мои желания

Автор: Lepore
Название:  Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
ISBN: 9783031124013
Издательство: Springer
Классификация:

ISBN-10: 3031124014
Обложка/Формат: Soft cover
Страницы: 123
Вес: 0.22 кг.
Дата издания: 03.11.2022
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 23 tables, color; 32 illustrations, color; 13 illustrations, black and white; vii, 123 p. 45 illus., 32 illus. in color.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Statistics
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
Дополнительное описание: - 1. Different Views of Interpretability. - 2. Model Interpretability, Explainability and Trust for Manufacturing 4.0. - 3. Interpretability via Random Forests. - 4. Interpretability in Generalized Additive Models.



Statistical Reinforcement Learning

Автор: Sugiyama
Название: Statistical Reinforcement Learning
ISBN: 1439856893 ISBN-13(EAN): 9781439856895
Издательство: Taylor&Francis
Рейтинг:
Цена: 13014.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.

Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.

  • Covers the range of reinforcement learning algorithms from a modern perspective
  • Lays out the associated optimization problems for each reinforcement learning scenario covered
  • Provides thought-provoking statistical treatment of reinforcement learning algorithms

The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.

This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.

Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th Internat

Автор: Reyes Mauricio, Henriques Abreu Pedro, Cardoso Jaime
Название: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th Internat
ISBN: 3030874435 ISBN-13(EAN): 9783030874438
Издательство: Springer
Рейтинг:
Цена: 7685.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: iMIMIC 2021 Workshop.- Interpretable Deep Learning for Surgical Tool Management.- Soft Attention Improves Skin Cancer Classification Performance.- Deep Gradient based on Collective Arti cial Intelligence for AD Diagnosis and Prognosis.- This explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks.- Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions.- The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data.- Voxel-level Importance Maps for Interpretable Brain Age Estimation.- TDA4MedicalData Workshop.- Lattice Paths for Persistent Diagrams.- Neighborhood complex based machine learning (NCML) models for drug design.- Predictive modelling of highly multiplexed tumour tissue images by graph neural networks.- Statistical modeling of pulmonary vasculatures with topological priors in CT volumes.- Topological Detection of Alzheimer's Disease using Betti Curves.

Interpretability of Machine Intelligence in Medical Image Computing

Автор: Reyes
Название: Interpretability of Machine Intelligence in Medical Image Computing
ISBN: 3031179757 ISBN-13(EAN): 9783031179754
Издательство: Springer
Рейтинг:
Цена: 7685.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book constitutes the refereed joint proceedings of the 5th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2022, held in September 2022, in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022. The 10 full papers presented at iMIMIC 2022 were carefully reviewed and selected from 24 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention.

Transparency and Interpretability for Learned Representations of Artificial Neural Networks

Автор: Meyes
Название: Transparency and Interpretability for Learned Representations of Artificial Neural Networks
ISBN: 365840003X ISBN-13(EAN): 9783658400033
Издательство: Springer
Рейтинг:
Цена: 11179.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AI’s decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed light on how to adopt an empirical neuroscience inspired approach to investigate a neural network’s learned representation in the same spirit as neuroscientific studies of the brain.

Statistical and Machine Learning Approaches for Network Analysis

Автор: Dehmer
Название: Statistical and Machine Learning Approaches for Network Analysis
ISBN: 0470195150 ISBN-13(EAN): 9780470195154
Издательство: Wiley
Рейтинг:
Цена: 17416.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: * Provides a general framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for graph classification. * The proposed methods are applied to different real data sets to demonstrate their ability.

Machine Learning and Statistical Modeling Approaches to Image Retrieval

Автор: Yixin Chen; Jia Li; James Z. Wang
Название: Machine Learning and Statistical Modeling Approaches to Image Retrieval
ISBN: 1475779305 ISBN-13(EAN): 9781475779301
Издательство: Springer
Рейтинг:
Цена: 13974.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results.

Statistical Reinforcement Learning

Автор: Sugiyama, Masashi
Название: Statistical Reinforcement Learning
ISBN: 0367575868 ISBN-13(EAN): 9780367575861
Издательство: Taylor&Francis
Рейтинг:
Цена: 6889.00 р.
Наличие на складе: Поставка под заказ.


ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
   В Контакте     В Контакте Мед  Мобильная версия