標(biāo)題: Titlebook: Human and Machine Learning; Visible, Explainable Jianlong Zhou,Fang Chen Textbook 2018 Springer International Publishing AG, part of Spring [打印本頁] 作者: cessation 時間: 2025-3-21 18:08
書目名稱Human and Machine Learning影響因子(影響力)
書目名稱Human and Machine Learning影響因子(影響力)學(xué)科排名
書目名稱Human and Machine Learning網(wǎng)絡(luò)公開度
書目名稱Human and Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Human and Machine Learning被引頻次
書目名稱Human and Machine Learning被引頻次學(xué)科排名
書目名稱Human and Machine Learning年度引用
書目名稱Human and Machine Learning年度引用學(xué)科排名
書目名稱Human and Machine Learning讀者反饋
書目名稱Human and Machine Learning讀者反饋學(xué)科排名
作者: Harpoon 時間: 2025-3-21 20:39 作者: 即席演說 時間: 2025-3-22 03:33 作者: 旅行路線 時間: 2025-3-22 06:28 作者: sebaceous-gland 時間: 2025-3-22 09:05 作者: reject 時間: 2025-3-22 13:24 作者: BOOM 時間: 2025-3-22 17:18
Critical Challenges for the Visual Representation of Deep Neural Networksout their interpretability. Visual representation is one way researchers are attempting to make sense of these models and their behaviour. The representation of neural networks raises questions which cross disciplinary boundaries. This chapter draws on a growing collection of interdisciplinary schol作者: delusion 時間: 2025-3-22 21:38 作者: critique 時間: 2025-3-23 05:12
Perturbation-Based Explanations of Prediction Models to neural networks and more general perturbation-based approaches which can be used with arbitrary prediction models. We present an overview of perturbation-based approaches, with focus on the most popular methods (EXPLAIN, IME, LIME). These methods support explanation of individual predictions but作者: 蜿蜒而流 時間: 2025-3-23 07:09 作者: staging 時間: 2025-3-23 10:55
Revealing User Confidence in Machine Learning-Based Decision Makingion making called . is proposed to show how human’s behaviour and physiological signals are used to reveal human cognition states in ML-based decision making. The chapter takes the revealing of user confidence in ML-based decision making as an example to demonstrate the effectiveness of the proposed作者: entrance 時間: 2025-3-23 16:16 作者: malign 時間: 2025-3-23 18:30 作者: Gudgeon 時間: 2025-3-24 02:05 作者: 空氣 時間: 2025-3-24 04:37
Group Cognition and Collaborative AIiscusses group cognition as a principle for designing collaborative AI. Group cognition is the ability to relate to other group members’ decisions, abilities, and beliefs. It thereby allows participants to adapt their understanding and actions to reach common objectives. Hence, it underpins collabor作者: CLOT 時間: 2025-3-24 08:39 作者: TRUST 時間: 2025-3-24 14:28 作者: growth-factor 時間: 2025-3-24 18:41
Patrick C. Shihklassischen Risiken wie Feuer, Explosion oder Naturgefahren an Versicherungen sind schon l?nger nicht mehr als alleinige Absicherung gegen Unternehmensrisiken ausreichend. Szenarien für politische Risiken, Hackerangriffe, Datendiebstahl sowie Ausfall von IT-Systemen, kritischer Infrastruktur oder vo作者: ETCH 時間: 2025-3-24 21:24 作者: relieve 時間: 2025-3-25 00:02 作者: amyloid 時間: 2025-3-25 06:27
ohe Geldstrafen, Klagen und Reputationsverlust für die betroffenen Unternehmen zur Folge hatten. Die Ausl?ser waren vor allem Korruptions- und Geldw?schef?lle. In jedem dieser F?lle wurde die Verantwortlichkeit der Organe und sonstiger Entscheidungstr?ger im Unternehmen thematisiert. Neben der Freih作者: angina-pectoris 時間: 2025-3-25 10:52 作者: VOK 時間: 2025-3-25 13:53
Jianlong Zhou,Kun Yu,Fang Chenrkungen zur effektiven Nutzung Die beste Information ist zu nichts nütze, wenn man sie nicht findet. Ausgehend von dieser Tatsache wurde das vorliegende Werk so gestaltet, da? es dem Leser m?glichst leicht f?llt, sich zu orientieren und spezielle Informationen schnell zu finden. Aufbau des Werkes ?B作者: 殺蟲劑 時間: 2025-3-25 16:22 作者: BLANK 時間: 2025-3-26 00:04
Joseph Lyons,Nhut Ho,Jeremy Friedman,Gene Alarcon,Svyatoslav Guznovhaltsübersicht der Sektionen und ihrer Kapitel (die mit der Folgelieferung Dezember ‘96 gelieferten Beitr?ge sind farbig unterlegt. ) Sektion 00, Einleitung 00. 00 Einleitung K D?TTINGER, U. LUTZ, K ROTH 00. 01 Inhaltsübersicht 00. 02 uto. r:enverzeic. , . . . . . . """-_ Sektion 01, Betriebswirt- s作者: attenuate 時間: 2025-3-26 02:10
Textbook 2018owers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the 作者: ARENA 時間: 2025-3-26 06:28
Transparency in Fair Machine Learning: the Case of Explainable Recommender Systemsfair models. We then review the taxonomy of explanation styles for recommender systems and review models that can provide explanations for their recommendations. We conclude by reviewing evaluation metrics for assessing the power of explainability in recommender systems.作者: MAUVE 時間: 2025-3-26 08:29
Beyond Human-in-the-Loop: Empowering End-Users with Transparent Machine Learningr than struggling with a development environment and new programming syntax or relying on disciplinary non-experts for essential technical help. This research explores a similar paradigm for scientists and end-users that can be thought of as End-User Data Analytics (EUDA), or Transparent Machine Learning (TML).作者: 有法律效應(yīng) 時間: 2025-3-26 16:26
Deep Learning for Plant Diseases: Detection and Saliency Map Visualisationlant diseases classification with an accuracy reaching .. Furthermore, we have proposed the use of saliency maps as a visualisation method to understand and interpret the CNN classification mechanism. This visualisation method increases the transparency of deep learning models and gives more insight into the symptoms of plant diseases.作者: 聽覺 時間: 2025-3-26 19:24 作者: panorama 時間: 2025-3-26 22:17 作者: 漂泊 時間: 2025-3-27 02:21
2D Transparency Space—Bring Domain Users and Machine Learning Experts Togethercy space which integrates domain users and ML experts together to make ML transparent. We identify typical Transparent ML (TML) challenges and discuss key obstacles to TML, which aim to inspire active discussions of making ML transparent with a systematic view in this timely field.作者: Noisome 時間: 2025-3-27 07:56
Effective Design in Human and Machine Learning: A Cognitive Perspective. A framework was proposed to advance the practice of machine learning focusing on transfer of knowledge in human deep learning with respect to the relations between human cognitive processes and machine learning.作者: Ointment 時間: 2025-3-27 10:34
Perturbation-Based Explanations of Prediction Models as their advantages and disadvantages. We illustrate practical issues and challenges in applying the explanation methodology in a business context on a practical use case of B2B sales forecasting in a company. We demonstrate how explanations can be used as a what-if analysis tool to answer relevant business questions.作者: 不可比擬 時間: 2025-3-27 17:36
Group Cognition and Collaborative AItion with humans: conversational grounding and theory of mind. These concepts are somewhat different from those already discussed in AI research. We outline some new implications for collaborative AI, aimed at extending skills and solution spaces and at improving joint cognitive and creative capacity.作者: HIKE 時間: 2025-3-27 19:03 作者: 內(nèi)行 時間: 2025-3-28 01:33
Do I Trust a Machine? Differences in User Trust Based on System Performanceceive the accuracy of the system and adjust their trust accordingly. The results also show notable differences between two groups of users and indicate a possible threshold in the acceptance of the system. This important learning can be leveraged by designers of practical systems for sustaining the desired level of user trust.作者: Estimable 時間: 2025-3-28 02:32
Trust and Transparency in Machine Learning-Based Clinical Decision Supporte trust in automation, but is hard to achieve in practice. This chapter discusses the clinical and technology related factors that influence clinician trust in automated systems, and can affect the need for transparency when developing machine learning-based clinical decision support systems.作者: profligate 時間: 2025-3-28 07:14
Jianlong Zhou,Fang ChenCreates a systematic view of relations between human and machine learning from the perspectives of visualisation, explanation, trustworthiness and transparency.Explores human aspects in machine learni作者: ONYM 時間: 2025-3-28 11:05 作者: Callus 時間: 2025-3-28 16:10
https://doi.org/10.1007/978-3-319-90403-0Machine Learning; Human Factors; Visualization; Explanation; Transparency作者: Pde5-Inhibitors 時間: 2025-3-28 19:05
978-3-030-08007-5Springer International Publishing AG, part of Springer Nature 2018作者: 旅行路線 時間: 2025-3-28 22:57
Human and Machine Learning978-3-319-90403-0Series ISSN 1571-5035 Series E-ISSN 2524-4477 作者: 使害怕 時間: 2025-3-29 06:08
Transparency Communication for Machine Learning in Human-Automation Interactiond agents using automated explanations. We will discuss the application of a particular ML method, reinforcement learning (RL), in Partially Observable Markov Decision Process (POMDP)-based agents, and the design of explanation algorithms for RL in POMDPs.作者: 神圣將軍 時間: 2025-3-29 09:45
Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomogras work on a general method for explaining arbitrary prediction models (classification or regression) to a general methodology for constructing a quasi-nomogram for a black-box prediction model. We show that for an additive model, such a quasi-nomogram is equivalent to the one we would construct if t作者: 朦朧 時間: 2025-3-29 14:24 作者: 爭議的蘋果 時間: 2025-3-29 16:15 作者: Discrete 時間: 2025-3-29 21:14
Textbook 2018stematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but al作者: faultfinder 時間: 2025-3-30 00:15
1571-5035 an and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but al978-3-030-08007-5978-3-319-90403-0Series ISSN 1571-5035 Series E-ISSN 2524-4477 作者: pineal-gland 時間: 2025-3-30 07:22
Mohammed Brahimi,Marko Arsenovic,Sohaib Laraba,Srdjan Sladojevic,Kamel Boukhalfa,Abdelouhab Moussaou作者: 窩轉(zhuǎn)脊椎動物 時間: 2025-3-30 11:23 作者: 彩色的蠟筆 時間: 2025-3-30 13:29 作者: 綠州 時間: 2025-3-30 19:43 作者: irritation 時間: 2025-3-30 23:12
Robert Zheng,Kevin Greenbergtimierung von Ressourcen und Mitteln im Unternehmen einen ad?quaten Schutz für benannte Gef?hrdungen aufbaut. Durch die Vorbereitung auf die Bedrohungen k?nnen die Auswirkungen reduziert oder auf ein akzeptiertes Ma? begrenzt und somit für das Unternehmen kontrollierbar und planbar gemacht werden.作者: Schlemms-Canal 時間: 2025-3-31 03:31
David V. Pynadath,Michael J. Barnes,Ning Wang,Jessie Y. C. ChenRolle als unverzichtbarer Risikopartner in einem sich ?ndernden Marktumfeld zu erhalten und weiter auszubauen. Diesbezüglich besteht die Notwendigkeit eines ganzheitlichen Ansatzes bei der Risikobeurteilung eines Unternehmens, der den Fokus auf die Kernbereiche Risiko, Kapital und Mensch richtet. Mi作者: 手工藝品 時間: 2025-3-31 09:05
