標(biāo)題: Titlebook: Robust Latent Feature Learning for Incomplete Big Data; Di Wu Book 2023 The Author(s), under exclusive license to Springer Nature Singapor [打印本頁] 作者: 手套 時間: 2025-3-21 16:57
書目名稱Robust Latent Feature Learning for Incomplete Big Data影響因子(影響力)
書目名稱Robust Latent Feature Learning for Incomplete Big Data影響因子(影響力)學(xué)科排名
書目名稱Robust Latent Feature Learning for Incomplete Big Data網(wǎng)絡(luò)公開度
書目名稱Robust Latent Feature Learning for Incomplete Big Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Robust Latent Feature Learning for Incomplete Big Data被引頻次
書目名稱Robust Latent Feature Learning for Incomplete Big Data被引頻次學(xué)科排名
書目名稱Robust Latent Feature Learning for Incomplete Big Data年度引用
書目名稱Robust Latent Feature Learning for Incomplete Big Data年度引用學(xué)科排名
書目名稱Robust Latent Feature Learning for Incomplete Big Data讀者反饋
書目名稱Robust Latent Feature Learning for Incomplete Big Data讀者反饋學(xué)科排名
作者: 破裂 時間: 2025-3-21 23:43
Robust Latent Feature Learning for Incomplete Big Data978-981-19-8140-1Series ISSN 2191-5768 Series E-ISSN 2191-5776 作者: GLUT 時間: 2025-3-22 00:55
Improve Robustness of Latent Feature Learning Using Double-Space,In a high dimensional and incomplete (HDI) matrix, the original data is sparse. Among numerous missing data estimation approaches [1–12], latent feature learning (LFL) is widely studied and adopted because of its high efficiency and scalability.作者: 責(zé)問 時間: 2025-3-22 08:17
Di WuExposes readers to a novel research perspective regarding incomplete big data analysis.Presents several robust latent feature learning methods for incomplete big data analysis.Achieves efficient and e作者: 硬化 時間: 2025-3-22 11:55 作者: rheumatism 時間: 2025-3-22 15:15 作者: accomplishment 時間: 2025-3-22 17:47
Basis of Latent Feature Learning, services are provided online. Such numerous online services lead to the problem of information overload [1, 2]. Then, an intelligent and efficient system is desired to address such problem [3, 4]. Therefore, as one of the most efficient and effective approaches for addressing information load, the recommender system has attracted much attention.作者: nauseate 時間: 2025-3-22 23:59
Improving Robustness of Latent Feature Learning Using ,-Norm,s) to filter the required information is a very challenging problem [5, 6]. Up to now, various methods have been proposed to implement an RS, among which collaborative filtering (CF) is very popular [7–13].作者: 棲息地 時間: 2025-3-23 02:06 作者: 無能的人 時間: 2025-3-23 07:11 作者: 用肘 時間: 2025-3-23 11:19
Conclusion and Outlook, and incomplete (HDI) data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing HDI data lies in addressing the uncertainty problem caused by their incomplete characteristics and some outliers (e.g., noises).作者: BRACE 時間: 2025-3-23 15:18
Robust Latent Feature Learning based on Smooth ,-norm,is usually represented by a matrix. For example, it is common to see a user-item rating matrix in RSs [6–9], where each row represents a specific user, each column represents a specific item, and each entry represents the user’s preference for an item.作者: CANE 時間: 2025-3-23 21:05
Data-characteristic-aware Latent Feature Learning,ems, a data-characteristic-aware latent factor (DCALF) model is proposed in [55]. Its main idea is towfold: (1) it first extracts the dense latent features from the original raw HDI data by an LFL model, and (2) it employs DPClust method [21] to simultaneously identify the neighborhoods and outliers of HDI data on the extracted latent features.作者: vocation 時間: 2025-3-23 22:15
Posterior-neighborhood-regularized Latent Feature Learning,ervices are often performed to retrieve QoS data [10, 11]. However, in real applications, the number of candidate services is usually large. Therefore, checking all candidate Web services is expensive, time-consuming, and therefore impractical [6, 12, 13].作者: 騷動 時間: 2025-3-24 04:02 作者: wall-stress 時間: 2025-3-24 09:37 作者: Heretical 時間: 2025-3-24 13:36
Robust Latent Feature Learning based on Smooth ,-norm, social networks, wireless sensor networks, and intelligent transportation. In these applications, the relationship between the two types of entities is usually represented by a matrix. For example, it is common to see a user-item rating matrix in RSs [6–9], where each row represents a specific user作者: muscle-fibers 時間: 2025-3-24 14:55
Improving Robustness of Latent Feature Learning Using ,-Norm,s) to filter the required information is a very challenging problem [5, 6]. Up to now, various methods have been proposed to implement an RS, among which collaborative filtering (CF) is very popular [7–13].作者: 無關(guān)緊要 時間: 2025-3-24 20:25
Data-characteristic-aware Latent Feature Learning,odel based on the neighborhood information of historical recorded data [15–17]. While they have some limitations as follows:To address the above problems, a data-characteristic-aware latent factor (DCALF) model is proposed in [55]. Its main idea is towfold: (1) it first extracts the dense latent fea作者: 間諜活動 時間: 2025-3-25 01:47
Posterior-neighborhood-regularized Latent Feature Learning,, you can select and recommend Web services that meet the quality of service requirements of potential users. Warm-up tests that directly invoke Web services are often performed to retrieve QoS data [10, 11]. However, in real applications, the number of candidate services is usually large. Therefore作者: ETCH 時間: 2025-3-25 03:33 作者: 亞麻制品 時間: 2025-3-25 07:44 作者: 拱形面包 時間: 2025-3-25 14:43
Book 2023sportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational e作者: mitral-valve 時間: 2025-3-25 15:51 作者: 證實(shí) 時間: 2025-3-25 22:40
Book 2023 based on smooth .L.1.-norm, improving robustness of latent feature learningusing .L.1.-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent作者: 反省 時間: 2025-3-26 02:41
Robust Latent Feature Learning for Incomplete Big Data作者: 歡笑 時間: 2025-3-26 07:12 作者: 鞭打 時間: 2025-3-26 11:12
Ben C. J. van Velthovenls Ausdruck sinnvollen physiologischen Zusammenspiels zwischen Hoch- und Niederdruck-System von . postuliert wurden, ist in bezug auf Regulation und Beurteilung des Gesamtkreislaufes ein neuer Gesichtspunkt deutlich geworden. Bei der Bewertung gest?rter Kreislaufverh?ltnisse mu? berücksichtigt werde作者: 不給啤 時間: 2025-3-26 15:09 作者: reptile 時間: 2025-3-26 20:30 作者: 使虛弱 時間: 2025-3-27 00:21
Information Extraction from?Visually Rich Documents Using Directed Weighted Graph Neural Networktation to capture relationships among various VRD components. In contrast to conventional methods relying on spatial proximity through Euclidean distance, our approach aims to enhance performance by introducing a novel representation of relationships using directed weighted graphs. The information e作者: medieval 時間: 2025-3-27 03:54 作者: 聚集 時間: 2025-3-27 07:49 作者: 雕鏤 時間: 2025-3-27 12:45 作者: 具體 時間: 2025-3-27 15:11 作者: 領(lǐng)帶 時間: 2025-3-27 21:28 作者: Endometrium 時間: 2025-3-27 22:11
,Die Werte der Aufkl?rung und die Politik von heute,?rkt, da? der Sozialdemokratie ein Kant not tut […], der aufzeigt, wo ihr scheinbarer Materialismus die h?chste und darum am leichtesten irreführende Ideologie ist, da? die Verachtung des Ideals, die Erhebung der materiellen Faktoten zu den omnipotenten M?chten der Entwicklung Selbstt?uschung ist.“作者: Neutropenia 時間: 2025-3-28 02:18
Vom neurologischen Symptom zur Diagnoset to more sophisticated levels of autonomy, expansion to other food subsystems beyond the culinary processes, and exploration of latent needs around HFI. The framework and further discussions are intended to better articulate, evaluate, and inform design and developments in HFI.作者: Picks-Disease 時間: 2025-3-28 07:01
Jack M. Loomis,Roberta L. Klatzky,Marios Avraamides,Yvonne Lippa,Reginald G. Golledgeibutions studied; (c) compare these parameters for different distributions, different territories, and different time frames; (d) measure, geographical discrepancy (“minimal distance?0 between pairs of distributions synthetically; and 9e) obtain averaged values from many of these distances taken tog作者: 考博 時間: 2025-3-28 11:03
How to Approach Ethics in Intelligent Decision Support Systemsl values as an underlying concept for a framework consisting in a set of rules that need to be part of any IDSS design. Applied to a decision process for candidate pre-selection (e.g. hiring), we illustrate the pitfalls that lead to biased decisions and methods for mitigation of “wrong decisions”, f作者: avenge 時間: 2025-3-28 17:07 作者: amorphous 時間: 2025-3-28 21:26 作者: 偽書 時間: 2025-3-29 01:13
https://doi.org/10.1007/978-3-030-66292-9sperm function; sperm production; sperm assessment; sperm morphology; comparative biology; spermatozoon; m作者: BINGE 時間: 2025-3-29 03:28
Software Foundation Libraries for Intelligent Systemschine learning, such as neural networks, hidden Markov models, and Bayesian networks. The libraries are being used in real data mining applications and products, and are actively being developed and extended.
