Interpretable Artificial Intelligence: A Perspective of Granular Computing, Pedrycz Witold, Chen Shyi-Ming
Автор: Krzysztof Cpa?ka Название: Design of Interpretable Fuzzy Systems ISBN: 3319850067 ISBN-13(EAN): 9783319850061 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание: The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms.
Автор: Krzysztof Cpa?ka Название: Design of Interpretable Fuzzy Systems ISBN: 3319528807 ISBN-13(EAN): 9783319528809 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms.
Research Motivations.- Research Objectives and Selected Approaches.- Challenges of WisTech (based on IGrC) for CAS Modeling, Controlling, and Monitoring.- Main Overview of Results.- Guide to the Contents of the Book.- The Concept of Complex System.- Examples of Complex Systems.- Concept of Complex Systems Engineering (CSE).- CSE Practice: CSE Crisis.- CSE Theory: Some Approaches.- TPGP: The Concept of the Theory - Practice Gap Problem.
Автор: Han Liu; Mihaela Cocea Название: Granular Computing Based Machine Learning ISBN: 3319888846 ISBN-13(EAN): 9783319888842 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Поставка под заказ.
Описание:
This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data.
Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries.
This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.
Автор: Yiyu Yao; Qinghua Hu; Hong Yu; Jerzy W. Grzymala-B Название: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing ISBN: 331925782X ISBN-13(EAN): 9783319257822 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Cyberspace is a ubiquitous realm interconnecting every aspect of modern society, enabled by broadband networks and wireless signals around us, existing within local area networks in our schools, hospitals and businesses, and within the massive grids that power most countries. Securing cyberspace to ensure the continuation of growing economies and to protect a nations way of life is a major concern for governments around the globe.This book contains papers presented at the NATO Advanced Research Workshop ARW entitled Best Practices and Innovative Approaches to Develop Cyber Security and Resiliency Policy Framework, held in Ohrid,
Описание: This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing.
Описание: iMIMIC 2020.- Assessing attribution maps for explaining CNN-based vertebral fracture classifiers.- Projective Latent Interventions for Understanding and Fine-tuning Classifiers.- Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging.- Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations.- Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations.- Explainable Disease Classification via weakly-supervised segmentation.- Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns.- Explainability for regression CNN in fetal head circumference estimation from ultrasound images.- MIL3ID 2020.- Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins.- Semi-supervised Instance Segmentation with a Learned Shape Prior.- COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs.- Semi-supervised Machine Learning with MixMatch and Equivalence Classes.- Non-contrast CT Liver Segmentation using CycleGAN Data Augmentation from Contrast Enhanced CT.- Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation.- A Case Study of Transfer of Lesion-Knowledge.- Transfer Learning With Joint Optimization for Label-Efficient Medical Image Anomaly Detection.- Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation.- HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification.- Semi-supervised classification of chest radiographs.- LABELS 2020.- Risk of training diagnostic algorithms on data with demographic bias.- Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks.- Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels.- EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology.- Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection.- Labeling of Multilingual Breast MRI Reports.- Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning.- Labelling imaging datasets on the basis of neuroradiology reports: a validation study.- Semi-Supervised Learning for Instrument Detection with a Class Imbalanced Dataset.- Paying Per-label Attention for Multi-label Extraction from Radiology Reports.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning· Explanation Methods in Deep Learning· Learning Functional Causal Models with Generative Neural Networks· Learning Interpreatable Rules for Multi-Label Classification· Structuring Neural Networks for More Explainable Predictions· Generating Post Hoc Rationales of Deep Visual Classification Decisions· Ensembling Visual Explanations· Explainable Deep Driving by Visualizing Causal Attention· Interdisciplinary Perspective on Algorithmic Job Candidate Search· Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
The book outlines selected projects conducted under the supervision of the author. Moreover, it discusses significant relations between Interactive Granular Computing (IGrC) and numerous dynamically developing scientific domains worldwide, along with features characteristic of the author's approach to IGrC. The results presented are a continuation and elaboration of various aspects of Wisdom Technology, initiated and developed in cooperation with Professor Andrzej Skowron.
Based on the empirical findings from these projects, the author explores the following areas: (a) understanding the causes of the theory and practice gap problem (TPGP) in complex systems engineering (CSE);
(b) generalizing computing models of complex adaptive systems (CAS) (in particular, natural computing models) by constructing an interactive granular computing (IGrC) model of networks of interrelated interacting complex granules (c-granules), belonging to a single agent and/or to a group of agents;
(c) developing methodologies based on the IGrC model to minimize the negative consequences of the TPGP.
The book introduces approaches to the above issues, using the proposed IGrC model. In particular, the IGrC model refers to the key mechanisms used to control the processes related to the implementation of CSE projects.
One of the main aims was to develop a mechanism of IGrC control over computations that model a project's implementation processes to maximize the chances of its success, while at the same time minimizing the emerging risks. In this regard, the IGrC control is usually performed by means of properly selected and enforced (among project participants) project principles. These principles constitute examples of c-granules, expressed by complex vague concepts (represented by c-granules too). The c-granules evolve with time (in particular, the meaning of the concepts is also subject of change). This methodology is illustrated using project principles applied by the author during the implementation of the POLTAX, AlgoTradix, Merix, and Excavio projects outlined in the book.
Автор: Tsau Young Lin; Yiyu Y. Yao; Lotfi A. Zadeh Название: Data Mining, Rough Sets and Granular Computing ISBN: 3790825085 ISBN-13(EAN): 9783790825084 Издательство: Springer Рейтинг: Цена: 27251.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw- ing together points (objects) which are related through similarity, proximity or functionality.
Автор: Witold Pedrycz Название: Granular Computing ISBN: 3790824879 ISBN-13(EAN): 9783790824872 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The landscape of granular computing is immensely rich and involves set theory (interval mathematics), fuzzy sets, rough sets, random sets linked together in a highly synergetic environment.
Автор: Hiroshi Sakai; Mihir Chakraborty; Aboul-Ella Hassa Название: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing ISBN: 3642106455 ISBN-13(EAN): 9783642106453 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2009, held in Delhi, India in December 2009 in conjunction with the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009.
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