標(biāo)題: Titlebook: Visual Knowledge Discovery and Machine Learning; Boris Kovalerchuk Book 2018 Springer International Publishing AG 2018 Intelligent Systems [打印本頁(yè)] 作者: commotion 時(shí)間: 2025-3-21 16:59
書目名稱Visual Knowledge Discovery and Machine Learning影響因子(影響力)
書目名稱Visual Knowledge Discovery and Machine Learning影響因子(影響力)學(xué)科排名
書目名稱Visual Knowledge Discovery and Machine Learning網(wǎng)絡(luò)公開(kāi)度
書目名稱Visual Knowledge Discovery and Machine Learning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Visual Knowledge Discovery and Machine Learning被引頻次
書目名稱Visual Knowledge Discovery and Machine Learning被引頻次學(xué)科排名
書目名稱Visual Knowledge Discovery and Machine Learning年度引用
書目名稱Visual Knowledge Discovery and Machine Learning年度引用學(xué)科排名
書目名稱Visual Knowledge Discovery and Machine Learning讀者反饋
書目名稱Visual Knowledge Discovery and Machine Learning讀者反饋學(xué)科排名
作者: 滴注 時(shí)間: 2025-3-21 21:48
into account the entire image. The functionality of the RC component is differentiable. Thus, it can be merged to the deep neural network, and trained end-to-end with other sub-networks. We achieve identification rates of 85.32% and 52.28% for sagittal and coronal views and localization distance of作者: 有權(quán)威 時(shí)間: 2025-3-22 03:29
Boris Kovalerchuk each axial slice. Second, the abdominal wall and psoas muscle are segmented by combining MALF with active shape models and deformable models. Third, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are measured to assess the areas of muscle and fat tissue. The pr作者: 名詞 時(shí)間: 2025-3-22 05:08 作者: PANIC 時(shí)間: 2025-3-22 10:30 作者: TEN 時(shí)間: 2025-3-22 15:38
Boris Kovalerchukrt of the process..The hydrodynamic characteristics of the entire flow field around different obstacles were investigated by a Laser-Doppler-Velocimeter (LDV) in order to determine a “critical pressure history“ P(t) for a single cavitation nucleus. Hydrodynamic scaling-up of both pressure and time a作者: 糾纏 時(shí)間: 2025-3-22 19:21 作者: preservative 時(shí)間: 2025-3-23 00:22
Boris Kovalerchuka “ready reference” of the key equations for the application of one very widely used theoretical strategy—the eigenfunction-expansion or “close-coupling“ method— to one very important class of problems: quantum scattering (at incident energies less than about 10 eV) from a closed-shell diatomic mole作者: Entirety 時(shí)間: 2025-3-23 02:31 作者: 最后一個(gè) 時(shí)間: 2025-3-23 06:55
Boris Kovalerchukarent. The first is that the primitive-variable formulation is preferable to the stream-function vorticity approach in terms of efficiency and ease of application. For inviscid flows the reason is clear because of the lower order of differentiation required in the primitive-variable formulation comp作者: 憎惡 時(shí)間: 2025-3-23 13:38
Boris Kovalerchukents) of the system by Gaussian elimination, .. This is so because the factorization process can be optimized quite well, while it is difficult to improve very much the performance of the back solver. The factorization time is by far the most expensive part when the . is used, while very often the s作者: 邊緣 時(shí)間: 2025-3-23 16:08
Boris Kovalerchuktations of the semantics in linguistic data using conventions about language structure, information from lexical resources (§5.5), and context from world knowledge in general. Even in the case of images with keywords, pre-processing can be used to remove less useful words, flag proper nouns, identif作者: HERE 時(shí)間: 2025-3-23 19:34 作者: LATHE 時(shí)間: 2025-3-24 02:06
Boris Kovalerchukations include pseudopotentials, statistical exchange approxi- mations, muffin-tin model potentials, and multiple scattering and cellular methods. In addition, many new approaches are being de- vised to deal with progressively more complex molecular and local- ized electronic state problems.978-1-4684-2015-9978-1-4684-2013-5作者: 柔軟 時(shí)間: 2025-3-24 03:09 作者: URN 時(shí)間: 2025-3-24 08:35
Book 2018ication, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science..作者: GROG 時(shí)間: 2025-3-24 13:32 作者: 凈禮 時(shí)間: 2025-3-24 14:54
General Line Coordinates (GLC), of GLCs include n-Gon, Circular, In-Line, Dynamic, and Bush Coordinates, which directly generalize Parallel and Radial Coordinates. Another class of GLCs described in this chapter is a class of reversible Paired Coordinates that includes Paired Orthogonal, Non-orthogonal, Collocated, Partially Coll作者: constitute 時(shí)間: 2025-3-24 19:15
Adjustable GLCs for Decreasing Occlusion and Pattern Simplification,asing the occlusion, and pattern simplification in different General Line Coordinates by adjusting GLCs to the given data via shifting, relocating, and scaling coordinates. ??In contrast, in Parallel and Radial Coordinates such adjustments of parameters to are more limited. These adjustment transfor作者: 對(duì)待 時(shí)間: 2025-3-25 02:07 作者: Enteropathic 時(shí)間: 2025-3-25 05:42
Discovering Visual Features and Shape Perception Capabilities in GLC,ter evaluates efficiency of the human visual system in discovering discriminating features for n-D data classification learning tasks in Closed Contour Paired Coordinates (traditional Stars/Radial Coordinates, and CPC Stars) in comparison with Parallel Coordinates. It is shown that Closed Contour Pa作者: 土產(chǎn) 時(shí)間: 2025-3-25 07:55 作者: Condense 時(shí)間: 2025-3-25 11:51
Knowledge Discovery and Machine Learning for Investment Strategy with CPC, tasks instead of complex cognitive tasks.? H?owever for cognitive tasks such as financial investment decision making, this opportunity faces the challenge that financial data are abstract multidimensional and multivariate, i.e., outside of traditional visual perception in 2-D or 3-D world. This cha作者: BRUNT 時(shí)間: 2025-3-25 18:24 作者: 袋鼠 時(shí)間: 2025-3-25 20:01 作者: Cacophonous 時(shí)間: 2025-3-26 00:30 作者: 我說(shuō)不重要 時(shí)間: 2025-3-26 06:02
Toward Virtual Data Scientist and Super-Intelligence with Visual Means,s a result, a huge number of Machine Learning (ML) tasks, which must be solved, dramatically exceeds the number of the data scientists, who can solve these tasks. Next, many ML tasks require the critical input, from the subject matter experts (SME), and end users/decision makers, who are not ML expe作者: tattle 時(shí)間: 2025-3-26 09:12
Comparison and Fusion of Methods and Future Research, we summarize some comparisons that were presented in other chapters. Next, the hybrid approach that fuses General Line Coordinates with other methods is summarized along with the outline of the future research.??作者: Iatrogenic 時(shí)間: 2025-3-26 15:13 作者: 悶熱 時(shí)間: 2025-3-26 20:18
1868-4394 serve n-D data with a focus on machine learning/data mining .This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, wh作者: 寬大 時(shí)間: 2025-3-27 00:05
Motivation, Problems and Approach,eversible lossy visual representations of n-D data along with their impact on efficiency of solving Data Mining/Machine Learning tasks. The approach concentrates on reversible representations along with the hybrid methodology to mitigate deficiencies of different representations?.作者: Observe 時(shí)間: 2025-3-27 05:07 作者: SEED 時(shí)間: 2025-3-27 05:19
Discovering Visual Features and Shape Perception Capabilities in GLC,s for classification these high-dimensional data. The chapter concludes with a description of the cooperative visualization approach to enhance Knowledge Discovery in solving Data Mining/Machine Learning tasks.?作者: gusher 時(shí)間: 2025-3-27 09:35
Pareto Front and General Line Coordinates,pter shows a way to accomplish this with GLC-L visualization method defined Chap. 7. It also shows a way to visualize the approximation set for the Pareto Front with Collocated Paired Coordinates, defined in Chap. 2 in comparison with Parallel Coordinates to assists in finding “best” Pareto points.作者: definition 時(shí)間: 2025-3-27 14:26
Toward Virtual Data Scientist and Super-Intelligence with Visual Means, to meet this Big data challenge, with a minimal contribution from data scientists. This chapter describes our vision of such a “virtual data scientist”, based on the visual approach of the General Line Coordinates.作者: DAUNT 時(shí)間: 2025-3-27 19:51 作者: 附錄 時(shí)間: 2025-3-28 01:00 作者: acrophobia 時(shí)間: 2025-3-28 02:17
Interactive Visual Classification, Clustering and Dimension Reduction with GLC-L,relations and dimension reduction. Classification and dimension reduction tasks from three domains, image processing, computer-aided medical diagnostics and finance (stock market), are used to illustrate this method.作者: 可行 時(shí)間: 2025-3-28 06:39 作者: zonules 時(shí)間: 2025-3-28 13:11
Intelligent Systems Reference Libraryhttp://image.papertrans.cn/v/image/983746.jpg作者: 無(wú)辜 時(shí)間: 2025-3-28 15:13
https://doi.org/10.1007/978-3-319-73040-0Intelligent Systems; Data Science; Knowledge Discovery; Visual Data Mining; Machine Learning; Multidimens作者: 串通 時(shí)間: 2025-3-28 19:36 作者: DALLY 時(shí)間: 2025-3-29 00:16 作者: 假 時(shí)間: 2025-3-29 06:13 作者: Flatter 時(shí)間: 2025-3-29 09:46 作者: 混合,攙雜 時(shí)間: 2025-3-29 13:21
Theoretical and Mathematical Basis of GLC,This chapter mathematically defines concepts that form various General Line Coordinates (GLCs). It provides relevant algorithms and statements that describe mathematical properties of GLCs and relations that GLCs represent.?The theoretical basis of a GLC is considered in connection with the Johnson-Lindenstrauss Lemma.作者: 頭腦冷靜 時(shí)間: 2025-3-29 18:06 作者: FUME 時(shí)間: 2025-3-29 20:18
owever, due to tissue overlaying and size of spinal radiographs, vertebrae localization and labeling are challenging and complicated. To address this, we propose a robust approach for landmark detection in large and noisy images and apply it on spinal radiographs. In this approach, the model has a h作者: 概觀 時(shí)間: 2025-3-30 03:34
