標(biāo)題: Titlebook: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery; Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2024 The [打印本頁(yè)] 作者: expenditure 時(shí)間: 2025-3-21 18:29
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書目名稱Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery讀者反饋
書目名稱Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery讀者反饋學(xué)科排名
作者: 無(wú)聊的人 時(shí)間: 2025-3-21 23:16 作者: 有惡臭 時(shí)間: 2025-3-22 00:56
Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery978-3-031-46549-9Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: HAVOC 時(shí)間: 2025-3-22 07:56
Boris Kovalerchuk,Kawa Nazemi,Ebad BanissiProvides recent research on Artificial Intelligence, Visualization, Visual Knowledge Discovery, and Visual Analytics.Is devoted to AI and Visualization‘for advancing Visual Knowledge Discover.Contains作者: Anticoagulants 時(shí)間: 2025-3-22 10:59 作者: 都相信我的話 時(shí)間: 2025-3-22 14:17
Factorization and Riccati Equationsss. Decision Trees (DTs) are essential in machine learning because they are used to understand many black box ML models including Deep Learning models. In this research, two new methods for creation and enhancement with complete visualizing Decision Trees as understandable models are suggested. Thes作者: podiatrist 時(shí)間: 2025-3-22 18:36 作者: reptile 時(shí)間: 2025-3-22 22:28
Factorization of Measurable Matrix Functionsdesigned for numeric data. This work focuses on developing numeric coding schemes for non-numeric attributes for ML algorithms to support accurate and explainable ML models, methods for lossless visualization of n-D non-numeric categorical data with visual rule discovery in these visualizations, and作者: notice 時(shí)間: 2025-3-23 05:06 作者: 牙齒 時(shí)間: 2025-3-23 05:39 作者: miscreant 時(shí)間: 2025-3-23 13:05
Objectives and Research Approach, interactive tools, they can enhance the cognitive processing of perceiving the complexity of such problems and help guide search algorithms toward good solutions. The present chapter provides an overview of the scientific works that applied information visualization techniques in the context of opt作者: Arthritis 時(shí)間: 2025-3-23 16:51 作者: recession 時(shí)間: 2025-3-23 20:14 作者: 灌輸 時(shí)間: 2025-3-24 01:41 作者: 財(cái)主 時(shí)間: 2025-3-24 05:57
Studies in Contemporary Economicsprediction can help improve traffic management, reduce congestion and pollution, and increase road safety. Mitigation solutions are usually used to soften the impact of this problem in most cities. In particular, the city of Lisbon has taken measures to reduce pollution by closing areas of the city 作者: 橢圓 時(shí)間: 2025-3-24 09:03 作者: Indent 時(shí)間: 2025-3-24 13:45
https://doi.org/10.1007/978-3-658-09918-3g need for research and development in methods and systems that utilize artificial intelligence to provide research communities with adequate tools that facilitate and encourage collaborative research. Many platforms focus on listing authors’ publications and showcasing them with citation scores. Th作者: capillaries 時(shí)間: 2025-3-24 15:13
Factors Driving Social Network Site Usaged of computational crime analysis. Public prosecutors, on the other hand, can usually rely only on databases (containing complaints, criminal records, or police reports) accessible via traditional textual interfaces that lack advanced and visually-intuitive information extraction functionalities. Th作者: larder 時(shí)間: 2025-3-24 19:30 作者: 剝皮 時(shí)間: 2025-3-25 00:00 作者: 補(bǔ)充 時(shí)間: 2025-3-25 05:45 作者: HAIL 時(shí)間: 2025-3-25 08:35 作者: 繁重 時(shí)間: 2025-3-25 13:24
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualizationdesigned for numeric data. This work focuses on developing numeric coding schemes for non-numeric attributes for ML algorithms to support accurate and explainable ML models, methods for lossless visualization of n-D non-numeric categorical data with visual rule discovery in these visualizations, and作者: 油氈 時(shí)間: 2025-3-25 18:41 作者: Compass 時(shí)間: 2025-3-25 22:26
Visual Knowledge Discovery with General Line Coordinates methods already exist, these methods are often unexplainable or perform poorly on complex data. This paper proposes Visual Knowledge Discovery approaches based on several forms of lossless General Line Coordinates. These are an expansion of the previously introduced General Line Coordinates Linear 作者: Pillory 時(shí)間: 2025-3-26 02:23 作者: 舔食 時(shí)間: 2025-3-26 04:53 作者: cortisol 時(shí)間: 2025-3-26 12:18
Computation of?Pixel-Oriented Grid Layout for?2D Datasets Using VRGridod, we propose a novel post-processing algorithm called VRGrid which allows the arrangement of any two-dimensional data in a grid while minimizing disformation of the input data. This method can be used with popular but overlap-prone projection methods such as t-SNE or MDS to obtain overlap-free and作者: chapel 時(shí)間: 2025-3-26 13:55 作者: 中古 時(shí)間: 2025-3-26 16:49
Road Traffic Flow Prediction with Visual Analyticsprediction can help improve traffic management, reduce congestion and pollution, and increase road safety. Mitigation solutions are usually used to soften the impact of this problem in most cities. In particular, the city of Lisbon has taken measures to reduce pollution by closing areas of the city 作者: 蚊子 時(shí)間: 2025-3-27 00:11
Guided Visual Analytics—A Visual Analytics Guidance Approach for?Systematic Reviews in?Researchr to make them usable for novices is one subject of current research works. One way to ease the user’s interaction with the systems is through guidance approaches. Guidance approaches aim to support the user while working with the system by providing targeted assistance. We present in this work a st作者: 衣服 時(shí)間: 2025-3-27 01:07 作者: 保全 時(shí)間: 2025-3-27 06:01 作者: 粗野 時(shí)間: 2025-3-27 11:23 作者: 旁觀者 時(shí)間: 2025-3-27 14:07 作者: fidelity 時(shí)間: 2025-3-27 21:27 作者: 陳舊 時(shí)間: 2025-3-27 22:11 作者: figment 時(shí)間: 2025-3-28 05:22 作者: 臨時(shí)抱佛腳 時(shí)間: 2025-3-28 07:32
Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Model visualize DT models more completely. These capabilities allow us to observe and analyze: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) sensitivity of each split threshold in the DT nodes, and (5) density of cases in parts of the n-作者: follicular-unit 時(shí)間: 2025-3-28 11:48 作者: intelligible 時(shí)間: 2025-3-28 17:25 作者: Macronutrients 時(shí)間: 2025-3-28 19:26 作者: endocardium 時(shí)間: 2025-3-29 02:13 作者: 極大的痛苦 時(shí)間: 2025-3-29 06:54
Relative Confusion Matrix: An Efficient Visualization for?the?Comparison of?Classification Modelsiminated classes and problematic classes of a single classifier, the very few works leverage the matrix structure of this visualization to compare several models at a class scale. In this paper, we present the Relative Confusion Matrix (RCM), a matrix-based visualization leveraging a color encoding 作者: 形上升才刺激 時(shí)間: 2025-3-29 10:51
Road Traffic Flow Prediction with Visual Analyticsedictive model. The model uses XGBoost to create a short-term time delay estimation for a region-of-interest. We find that our approach is able to achieve high Mean Squared Error (R.) results and low Mean Absolute Error (MAE). Additionally, we perform a user study to assess the quality of traffic fl作者: nitric-oxide 時(shí)間: 2025-3-29 13:58 作者: carotid-bruit 時(shí)間: 2025-3-29 19:23
Integrating Machine Learning in?Visual Analytics for?Supporting Collaboration in?Scienceons, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the 作者: BRIBE 時(shí)間: 2025-3-29 20:21 作者: AIL 時(shí)間: 2025-3-30 01:04
Designing and Evaluating Context-Sensitive Visualization Models for?Deep Learning Text Classifierso gauge the usefulness of the extracted insights for explaining the models. Additionally, visualizing discrepancies in the knowledge extracted by different models becomes crucial for effective ranking purposes. This is an area of research with very few available results. In this work, we investigate作者: persistence 時(shí)間: 2025-3-30 05:38 作者: 技術(shù) 時(shí)間: 2025-3-30 09:36
Factorization and Riccati Equations visualize DT models more completely. These capabilities allow us to observe and analyze: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) sensitivity of each split threshold in the DT nodes, and (5) density of cases in parts of the n-作者: 障礙物 時(shí)間: 2025-3-30 15:30
Canonical Factorization and Applicationsaracteristics of page block classification data led to the development of an algorithm for imbalanced high-resolution data with multiple classes, which exploits the decision trees as a model design facilitator producing a model, which is more general than a decision tree. This work accelerates the o作者: 是限制 時(shí)間: 2025-3-30 19:10
Factorization of Measurable Matrix Functionsessfully evaluated in multiple computational experiments. This work is one of the steps to the full scope ML algorithms for mixed data supported by lossless visualization of n-D data in General Line Coordinates beyond Parallel Coordinates.作者: 整潔漂亮 時(shí)間: 2025-3-30 21:21
Operator Theory: Advances and Applicationsn reduction and visualization have been established. The capability of end users to find and observe hyperblocks, as well as the ability of side-by-side visualizations to make patterns evident, are among major advantages of hyperblock technology and the Hyper algorithm. A new method to visualize inc作者: Pudendal-Nerve 時(shí)間: 2025-3-31 02:17
Albrecht B?ttcher,Sergei Grudsky Experiments across multiple benchmark datasets show that this Visual Knowledge Discovery method can compete with other visual and computational Machine Learning algorithms while improving both interpretability and accuracy in linear and non-linear classifications. Major benefits from these expansio作者: malapropism 時(shí)間: 2025-3-31 06:26
