標(biāo)題: Titlebook: Broad Learning Through Fusions; An Application on So Jiawei Zhang,Philip S. Yu Textbook 2019 Springer Nature Switzerland AG 2019 data minin [打印本頁(yè)] 作者: 果園 時(shí)間: 2025-3-21 17:04
書目名稱Broad Learning Through Fusions影響因子(影響力)
書目名稱Broad Learning Through Fusions影響因子(影響力)學(xué)科排名
書目名稱Broad Learning Through Fusions網(wǎng)絡(luò)公開度
書目名稱Broad Learning Through Fusions網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Broad Learning Through Fusions被引頻次
書目名稱Broad Learning Through Fusions被引頻次學(xué)科排名
書目名稱Broad Learning Through Fusions年度引用
書目名稱Broad Learning Through Fusions年度引用學(xué)科排名
書目名稱Broad Learning Through Fusions讀者反饋
書目名稱Broad Learning Through Fusions讀者反饋學(xué)科排名
作者: ELUC 時(shí)間: 2025-3-21 22:15 作者: 索賠 時(shí)間: 2025-3-22 02:25
neue betriebswirtschaftliche forschung (nbf)s different parts of an elephant in their life journeys. In turn, each blind man creates his own version of reality from that limited experiences and perspectives. Instead of explaining its philosophical meanings, we indent to use this story to illustrate the current situations that both the academia and industry are facing about ., ., and ..作者: Melodrama 時(shí)間: 2025-3-22 07:01 作者: Control-Group 時(shí)間: 2025-3-22 12:35 作者: 尊敬 時(shí)間: 2025-3-22 16:42 作者: incarcerate 時(shí)間: 2025-3-22 19:58
Jiawei Zhang,Philip S. YuThis book provides an introduction to broad learning, focusing on the fundamental concepts, learning tasks, and methodologies to build learning models for data fusion, and knowledge discovery..It exam作者: 和音 時(shí)間: 2025-3-22 22:25
http://image.papertrans.cn/b/image/191216.jpg作者: 宇宙你 時(shí)間: 2025-3-23 01:57
neue betriebswirtschaftliche forschung (nbf)s different parts of an elephant in their life journeys. In turn, each blind man creates his own version of reality from that limited experiences and perspectives. Instead of explaining its philosophical meanings, we indent to use this story to illustrate the current situations that both the academi作者: 重力 時(shí)間: 2025-3-23 08:21 作者: figurine 時(shí)間: 2025-3-23 10:35 作者: 解決 時(shí)間: 2025-3-23 16:49
Erkl?rungsmodell zum Netzwerkmarketing extremely challenging, expensive (in human efforts, time, and money costs), and tedious, and the scale of the real-world online social networks involving millions even billions of users also renders the training data labeling much more difficult. In this chapter, we will introduce several approache作者: anarchist 時(shí)間: 2025-3-23 19:47 作者: 高度 時(shí)間: 2025-3-24 00:22 作者: 按時(shí)間順序 時(shí)間: 2025-3-24 03:08
Marketing Action for Changing Times, The creation of the Internet and online social networks has rapidly facilitated the communication among people. Via the interactions among users in online social networks, information can easily be propagated from one user to other users. For instance, in recent years, online social networks have b作者: 臨時(shí)抱佛腳 時(shí)間: 2025-3-24 08:22 作者: Ccu106 時(shí)間: 2025-3-24 11:38
Marketing Ideology and Mass Media,presented as massive and complex networks. The representative examples include online social networks, like Facebook and Twitter, academic retrieval sites, like DBLP and Google Scholar, as well as bio-medical data, e.g., human brain networks. These networks/graphs are usually very challenging to han作者: 適宜 時(shí)間: 2025-3-24 15:25 作者: menopause 時(shí)間: 2025-3-24 21:37 作者: predict 時(shí)間: 2025-3-24 23:54
Machine Learning Overview the discovery of new facts and theories through observation and experimentation. Learning is one of the most important skills that mankind can master, which also renders us different from the other animals on this planet. To provide an example, according to our past experiences, we know the sun ris作者: CHIDE 時(shí)間: 2025-3-25 03:58
Supervised Network Alignmentocial network can be represented as a heterogeneous network containing abundant information about: who, where, when, and what, i.e., who the users are, where they have been to, what they have done, and when they did these activities. Different online social networks can provide unique social network作者: noxious 時(shí)間: 2025-3-25 09:45 作者: 碎片 時(shí)間: 2025-3-25 14:16
Semi-supervised Network Alignmentain is usually of a small size compared with the network scale, and most of the potential anchor links are unlabeled actually. For instance, given the Facebook and Twitter networks containing millions or billions of users, identifying a very small training set merely with hundreds of correct anchor 作者: heterodox 時(shí)間: 2025-3-25 18:20
Community Detectionnt interactions with each other, while those in different groups will have less interactions on the other hand. Formally, such social groups form by users in online social networks are called the online social communities. Online social communities will partition the network into a number of compone作者: 散布 時(shí)間: 2025-3-25 22:43 作者: 十字架 時(shí)間: 2025-3-26 03:44 作者: 多產(chǎn)魚 時(shí)間: 2025-3-26 05:16
Network Embeddingpresented as massive and complex networks. The representative examples include online social networks, like Facebook and Twitter, academic retrieval sites, like DBLP and Google Scholar, as well as bio-medical data, e.g., human brain networks. These networks/graphs are usually very challenging to han作者: 不合 時(shí)間: 2025-3-26 10:41
Frontier and Future Directionsmain parts, where the first 3 parts include 6 main research directions about broad learning based social network mining problems, including (1) ., (2) ., (3) ., (4) ., (5) ., and (6) .. These problems introduced in this book are all very important for many concrete real-world social network applicat作者: Pantry 時(shí)間: 2025-3-26 16:04 作者: Benign 時(shí)間: 2025-3-26 20:15
Erkl?rungsmodell zum Netzwerkmarketing, which also renders us different from the other animals on this planet. To provide an example, according to our past experiences, we know the sun rises from the east and falls to the west; the moon rotates around the earth; 1 year has 365 days, which are all knowledge we derive from our past life experiences.作者: 起草 時(shí)間: 2025-3-27 00:42 作者: 不持續(xù)就爆 時(shí)間: 2025-3-27 04:49 作者: palpitate 時(shí)間: 2025-3-27 05:16
Unsupervised Network Alignmentving millions even billions of users also renders the training data labeling much more difficult. In this chapter, we will introduce several approaches to resolve the . problem based on the unsupervised learning setting instead, where no labeled training data will be needed in model building.作者: Conscientious 時(shí)間: 2025-3-27 09:41 作者: entreat 時(shí)間: 2025-3-27 17:29
Community Detectionnts, where the intra-community social connections are usually far more dense compared with the inter-community social connections. Meanwhile, from the mathematical representation perspective, due to these online social communities, the social network adjacency matrix tend to be not only sparse but also low-rank.作者: upstart 時(shí)間: 2025-3-27 20:21
Frontier and Future Directionsions and services. A number of state-of-the-art algorithms have been proposed to solve these problems, which are introduced in great detail in this book. . is a very promising research area, and several potential future development directions about broad learning will be illustrated in the following sections.作者: receptors 時(shí)間: 2025-3-28 01:07
Textbook 2019ment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding..作者: 形容詞 時(shí)間: 2025-3-28 03:11
Erkl?rungsmodell zum Netzwerkmarketingd in Chap. .. On the other hand, completely ignoring the (small) set of labeled anchor links, just like the . models introduced in Chap. ., may also create lots of problems, since these labeled anchor links can provide important signals for the network alignment model building.作者: HOWL 時(shí)間: 2025-3-28 09:22 作者: 斜 時(shí)間: 2025-3-28 10:51 作者: indubitable 時(shí)間: 2025-3-28 18:22
Semi-supervised Network Alignmentd in Chap. .. On the other hand, completely ignoring the (small) set of labeled anchor links, just like the . models introduced in Chap. ., may also create lots of problems, since these labeled anchor links can provide important signals for the network alignment model building.作者: humectant 時(shí)間: 2025-3-28 19:08 作者: Ergots 時(shí)間: 2025-3-29 01:20
Network Embedding diverse attributes (attached to the nodes or links). For instance, the Facebook social network involves more than 1 billion active users; DBLP contains about 2.8 billions of papers; and human brain has more than 16 billion neurons.作者: 蚊子 時(shí)間: 2025-3-29 06:28 作者: 改變 時(shí)間: 2025-3-29 09:36
Kritische Würdigung und Ausblick services for the users. For instance, Facebook is a general public social sharing site, Twitter is a micro blogging social site mainly about short posts, Foursquare is a location based social network, and LinkedIn is a business oriented professional social network site.作者: 鐵砧 時(shí)間: 2025-3-29 11:48 作者: hazard 時(shí)間: 2025-3-29 18:32
https://doi.org/10.1007/978-1-349-19147-5ions and services. A number of state-of-the-art algorithms have been proposed to solve these problems, which are introduced in great detail in this book. . is a very promising research area, and several potential future development directions about broad learning will be illustrated in the following sections.作者: implore 時(shí)間: 2025-3-29 20:22
Viral Marketing the cell phone. Such information will propagate to their friends and followers, who may get activated to purchase the cell phone. Commercial promotions via the online social networks have become more and more important in recent years, which even surpass the traditional print media (like newspaper,作者: nostrum 時(shí)間: 2025-3-30 00:11
