標題: Titlebook: Deep Learning in Mining of Visual Content; Akka Zemmari,Jenny Benois-Pineau Book 2020 The Author(s), under exclusive license to Springer N [打印本頁] 作者: minuscule 時間: 2025-3-21 17:00
書目名稱Deep Learning in Mining of Visual Content影響因子(影響力)
書目名稱Deep Learning in Mining of Visual Content影響因子(影響力)學(xué)科排名
書目名稱Deep Learning in Mining of Visual Content網(wǎng)絡(luò)公開度
書目名稱Deep Learning in Mining of Visual Content網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning in Mining of Visual Content被引頻次
書目名稱Deep Learning in Mining of Visual Content被引頻次學(xué)科排名
書目名稱Deep Learning in Mining of Visual Content年度引用
書目名稱Deep Learning in Mining of Visual Content年度引用學(xué)科排名
書目名稱Deep Learning in Mining of Visual Content讀者反饋
書目名稱Deep Learning in Mining of Visual Content讀者反饋學(xué)科排名
作者: 哄騙 時間: 2025-3-21 22:29
978-3-030-34375-0The Author(s), under exclusive license to Springer Nature Switzerland AG 2020作者: Addictive 時間: 2025-3-22 03:58 作者: 虛弱的神經(jīng) 時間: 2025-3-22 07:02
Aidan Beggs,Alexandros Kapravelosd dimension which finally allows a classification decision. We are interested in two operations: convolution and pooling and trace analogy with these operations in a classical Image Processing framework.作者: 流逝 時間: 2025-3-22 11:17
https://doi.org/10.1007/978-3-030-22038-9der those designed for particular data: images. First of all we will expose some general principles, then go into detail layer-by-layer and finally briefly overview most popular convolutional neural networks architectures.作者: 錫箔紙 時間: 2025-3-22 15:07 作者: 錫箔紙 時間: 2025-3-22 21:04 作者: Phenothiazines 時間: 2025-3-22 23:57 作者: Graphite 時間: 2025-3-23 01:23
SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/d/image/264624.jpg作者: mendacity 時間: 2025-3-23 06:14
Michael Brengel,Christian Rossowg consists in grouping similar data points in the description space thus inducing a structure on it. Then the data model can be expressed in terms of space partition. Probably, the most popular of such grouping algorithms in visual content mining is the K-means approach introduced by MacQueen as ear作者: Monolithic 時間: 2025-3-23 12:45 作者: 妨礙 時間: 2025-3-23 16:20
Aidan Beggs,Alexandros Kapravelosd dimension which finally allows a classification decision. We are interested in two operations: convolution and pooling and trace analogy with these operations in a classical Image Processing framework.作者: 橡子 時間: 2025-3-23 19:13
https://doi.org/10.1007/978-3-030-22038-9der those designed for particular data: images. First of all we will expose some general principles, then go into detail layer-by-layer and finally briefly overview most popular convolutional neural networks architectures.作者: convert 時間: 2025-3-24 01:06 作者: 上漲 時間: 2025-3-24 04:22 作者: 結(jié)合 時間: 2025-3-24 07:08 作者: cogitate 時間: 2025-3-24 12:18 作者: 搜集 時間: 2025-3-24 15:56 作者: Gustatory 時間: 2025-3-24 21:30
Dynamic Content Mining,of possible classes. Such networks have no notion of order in time nor in memory. That is they are not suitable for dynamic content mining like speech recognition, video processing, etc. In this chapter we introduce models able to handle temporality of visual content.作者: 勤勞 時間: 2025-3-25 01:40
Case Study for Digital Cultural Content Mining,hitectural styles and specific architectural structures. We are interested in attention mechanisms in Deep CNNs and explain how real visual attention maps built upon human gaze fixations can help in the training of deep neural networks.作者: FRET 時間: 2025-3-25 04:46
Michael Brengel,Christian RossowArtificial neural networks consist of distributed information processing units. In this chapter, we define the components of such networks. We will first introduce the elementary unit: the formal neuron proposed by McCulloch and Pitts. Further we will explain how such units can be assembled to design simple neural networks.作者: Emasculate 時間: 2025-3-25 08:01
Neural Networks from Scratch,Artificial neural networks consist of distributed information processing units. In this chapter, we define the components of such networks. We will first introduce the elementary unit: the formal neuron proposed by McCulloch and Pitts. Further we will explain how such units can be assembled to design simple neural networks.作者: 嫻熟 時間: 2025-3-25 13:19
Michael Brengel,Christian Rossowning approach is a part of the family of supervised learning methods designed both for classification and regression. In this very short chapter we will focus on the formal definition of supervised learning approach, but also on fundamentals of evaluation of classification algorithms as the evaluation metrics will be used further in the book.作者: OREX 時間: 2025-3-25 16:33
Supervised Learning Problem Formulation,ning approach is a part of the family of supervised learning methods designed both for classification and regression. In this very short chapter we will focus on the formal definition of supervised learning approach, but also on fundamentals of evaluation of classification algorithms as the evaluation metrics will be used further in the book.作者: Oration 時間: 2025-3-25 23:56 作者: BLANC 時間: 2025-3-26 02:48 作者: 提煉 時間: 2025-3-26 07:12 作者: 夾克怕包裹 時間: 2025-3-26 08:40 作者: 阻塞 時間: 2025-3-26 13:02 作者: 幸福愉悅感 時間: 2025-3-26 18:07 作者: FUSC 時間: 2025-3-26 22:59
Supervised Learning Problem Formulation,g consists in grouping similar data points in the description space thus inducing a structure on it. Then the data model can be expressed in terms of space partition. Probably, the most popular of such grouping algorithms in visual content mining is the K-means approach introduced by MacQueen as ear作者: Vasodilation 時間: 2025-3-27 02:38
Optimization Methods,the loss function. Most of them are iterative and operate by decreasing the loss function following a descent direction. These methods solve the problem when the loss function is supposed to be convex. The main idea can be expressed simply as follows: starting from initial arbitrary (or randomly) ch作者: 一條卷發(fā) 時間: 2025-3-27 09:08
Deep in the Wild,d dimension which finally allows a classification decision. We are interested in two operations: convolution and pooling and trace analogy with these operations in a classical Image Processing framework.作者: GLUE 時間: 2025-3-27 12:03 作者: 機密 時間: 2025-3-27 15:53 作者: 可忽略 時間: 2025-3-27 20:47 作者: PET-scan 時間: 2025-3-28 01:49
Introducing Domain Knowledge,is particular application of medical imaging domain, Deep NNs have become the mandatory tool. In this chapter we give some highlights on how the usual steps in design of a Deep Neural Network classifier are implemented in the case when domain knowledge has to be considered. But more than that: faith作者: 思考 時間: 2025-3-28 03:33
2191-5768 eep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to comp978-3-030-34375-0978-3-030-34376-7Series ISSN 2191-5768 Series E-ISSN 2191-5776 作者: GRATE 時間: 2025-3-28 07:54 作者: 孤僻 時間: 2025-3-28 11:55 作者: 上下倒置 時間: 2025-3-28 18:11 作者: Mawkish 時間: 2025-3-28 20:30
Die Wirkm?chtigkeit unternehmensethischer ManagementkonzepteQualitative Fallanal
