標(biāo)題: Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Andrea Torsello,Luca Rossi,Antonio Robles-Kelly Conference [打印本頁(yè)] 作者: intern 時(shí)間: 2025-3-21 19:23
書目名稱Structural, Syntactic, and Statistical Pattern Recognition影響因子(影響力)
書目名稱Structural, Syntactic, and Statistical Pattern Recognition影響因子(影響力)學(xué)科排名
書目名稱Structural, Syntactic, and Statistical Pattern Recognition網(wǎng)絡(luò)公開(kāi)度
書目名稱Structural, Syntactic, and Statistical Pattern Recognition網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Structural, Syntactic, and Statistical Pattern Recognition被引頻次
書目名稱Structural, Syntactic, and Statistical Pattern Recognition被引頻次學(xué)科排名
書目名稱Structural, Syntactic, and Statistical Pattern Recognition年度引用
書目名稱Structural, Syntactic, and Statistical Pattern Recognition年度引用學(xué)科排名
書目名稱Structural, Syntactic, and Statistical Pattern Recognition讀者反饋
書目名稱Structural, Syntactic, and Statistical Pattern Recognition讀者反饋學(xué)科排名
作者: SYN 時(shí)間: 2025-3-21 21:22
https://doi.org/10.1007/978-3-030-73973-7artificial intelligence; computer networks; computer science; computer systems; computer vision; directed作者: 引導(dǎo) 時(shí)間: 2025-3-22 03:40
978-3-030-73972-0Springer Nature Switzerland AG 2021作者: refraction 時(shí)間: 2025-3-22 07:57
Structural, Syntactic, and Statistical Pattern Recognition978-3-030-73973-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: jet-lag 時(shí)間: 2025-3-22 12:01 作者: 有罪 時(shí)間: 2025-3-22 13:41 作者: 燒瓶 時(shí)間: 2025-3-22 17:59 作者: 假裝是我 時(shí)間: 2025-3-22 23:47 作者: Servile 時(shí)間: 2025-3-23 05:09 作者: 重力 時(shí)間: 2025-3-23 07:05
Boyuan Wang,Lixin Cui,Lu Bai,Edwin R. Hancock Qualit?tssicherung (QM-System) nachweisen kann. Der Nachweis wird durch ein Qualit?tsmanagement-Handbuch (QM-Handbuch) erbracht, in dem die Qualit?tspolitik des Unternehmens verbindlich festgelegt und das vorhandene QM-System ausführlich beschrieben ist. Inhalt und Umfang der zu führenden Nachweise作者: Canopy 時(shí)間: 2025-3-23 13:04 作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-23 17:11 作者: 阻止 時(shí)間: 2025-3-23 18:27
Complex-Valued Embeddings of Generic Proximity Datamilarity measure, which may not obey particular mathematical properties. This renders many machine learning methods invalid, leading to convergence problems and the loss of generalization behavior. In many cases, the preferred dissimilarity measure is not metric. If the input data are non-vectorial,作者: 同義聯(lián)想法 時(shí)間: 2025-3-23 23:01 作者: 痛得哭了 時(shí)間: 2025-3-24 05:53 作者: 地殼 時(shí)間: 2025-3-24 07:30 作者: Eructation 時(shí)間: 2025-3-24 11:06 作者: 噴出 時(shí)間: 2025-3-24 18:24 作者: scoliosis 時(shí)間: 2025-3-24 22:32
Augmenting Graph Convolutional Neural Networks with Highpass Filters for the convolutional layer and consider the case of directed graphs. This allows for graph spectral theory and the connections between eigenfunctions over the graph and Fourier analysis to employ graph signal processing to obtain an architecture that “concatenates” low and high-pass filters to pro作者: Bumble 時(shí)間: 2025-3-25 00:22
Selecting Features from Time Series Using Attention-Based Recurrent Neural Networksemely large amounts of such data are being generated every second. In this paper, we introduce the recurrent neural networks equipped with attention modules that quantify the importance of features, hence can be employed to select only an informative subset of all available features. Additionally, o作者: PRISE 時(shí)間: 2025-3-25 05:32
Feature Extraction Functions for Neural Logic Rule Learningtracting functions for integrating human knowledge abstracted as logic rules into the predictive behaviour of a neural network. These functions are embodied as programming functions, which represent the applicable domain knowledge as a set of logical instructions and provide a modified distribution 作者: 知道 時(shí)間: 2025-3-25 09:46
Learning High-Resolution Domain-Specific Representations with a GAN Generator this work we study representations learnt by a GAN generator. First, we show that these representations can be easily projected onto semantic segmentation map using a lightweight decoder. We find that such semantic projection can be learnt from just a few annotated images. Based on this finding, we作者: 正論 時(shí)間: 2025-3-25 15:02
Predicting Polypharmacy Side Effects Through a Relation-Wise Graph Attention Networkortant to have reliable tools able to predict if the activity of a drug could unfavorably change when combined with others. State-of-the-art methods face this problem as a link prediction task on a multilayer graph describing drug-drug interactions (DDI) and protein-protein interactions (PPI), since作者: 拋媚眼 時(shí)間: 2025-3-25 19:53
LGL-GNN: Learning Global and Local Information for Graph Neural Networksgraph classification tasks. Our idea is to concatenate the convolution results of the deep graph convolutional network and the motif-based subgraph convolutional network layer by layer, and give attention weights to global features and local features. We hope that this method can alleviate the over-作者: 爭(zhēng)論 時(shí)間: 2025-3-25 21:16
Graph Transformer: Learning Better Representations for Graph Neural Networksce on many graph classification tasks. However, most state-of-the-art GNNs face the challenge of the over-smoothing problem and cannot learn latent relations between distant vertices well. To overcome this problem, we develop a novel Graph Transformer (GT) unit to learn latent relations timely. In a作者: Gesture 時(shí)間: 2025-3-26 01:12
Weighted Network Analysis Using the Debye Modelensively used to explore network structure. One of the shortcomings of this model is that it is couched in terms of unweighted edges. To overcome this problem and to extend the utility of this type of analysis, in this paper, we explore how the Debye solid model can be used to describe the probabili作者: Heterodoxy 時(shí)間: 2025-3-26 06:02
Estimating the Manifold Dimension of a Complex Network Using Weyl’s Lawtribution to the way the networks respond to diffusion and percolation processes. In this paper we propose a way to estimate the dimension of the manifold underlying a network that is based on Weyl’s law, a mathematical result that describes the asymptotic behaviour of the eigenvalues of the graph L作者: 慟哭 時(shí)間: 2025-3-26 11:09 作者: 裂縫 時(shí)間: 2025-3-26 13:00
Augmenting Graph Convolutional Neural Networks with Highpass Filters to graph spectral methods, Fourier analysis and graph signal processing. Here, we illustrate the utility of our graph convolutional approach to the classification using citation datasets and knowledge graphs. The results show that our method provides a margin of improvement over the alternative.作者: 輕打 時(shí)間: 2025-3-26 18:21
Feature Extraction Functions for Neural Logic Rule Learningnot require any kind of special mathematical encoding, which makes our method very general and flexible in nature. We illustrate the performance of our approach for sentiment classification and compare our results to those obtained using two baselines.作者: 可觸知 時(shí)間: 2025-3-27 00:11
Graph Transformer: Learning Better Representations for Graph Neural Networksnt connections well and form better representations for graphs. Moreover, the proposed Graph Transformer with Mixed Network (GTMN) can learn both local and global information simultaneously. Experiments on standard graph classification benchmarks demonstrate that our proposed approach performs better when compared with other competing methods.作者: 破譯 時(shí)間: 2025-3-27 01:56
Estimating the Manifold Dimension of a Complex Network Using Weyl’s Lawlf-similarity. Through an extensive set of experiments on both synthetic and real-world networks we show that our approach is able to correctly estimate the manifold dimension. We compare this with alternative methods to compute the fractal dimension and we show that our approach yields a better estimate on both synthetic and real-world examples.作者: tariff 時(shí)間: 2025-3-27 06:04 作者: 格子架 時(shí)間: 2025-3-27 11:48 作者: Palpitation 時(shí)間: 2025-3-27 16:01
LGL-GNN: Learning Global and Local Information for Graph Neural Networkssmoothing problem when the depth of the neural networks increases, and the introduction of motif for local convolution can better learn local neighborhood features with strong connectivity. Finally, our experiments on standard graph classification benchmarks prove the effectiveness of the model.作者: 輕打 時(shí)間: 2025-3-27 21:51
Conference proceedings 20212020, held in Padua, Italy, in January 2021...The 35 papers presented in this volume were carefully reviewed and selected from 81 submissions...The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural ma作者: 充滿裝飾 時(shí)間: 2025-3-27 23:02 作者: 瘋狂 時(shí)間: 2025-3-28 03:22
Exponential Weighted Moving Average of Time Series in Arbitrary Spaces with Application to Stringsl case of weighted mean computation. We develop three computation methods. In addition to the direct computation in the original space, we particularly study an approach to embedding the data items of a time series into vector space. The feasibility of our EWMA computation framework is exemplarily demonstrated on strings.作者: GUMP 時(shí)間: 2025-3-28 06:28 作者: 暫時(shí)休息 時(shí)間: 2025-3-28 13:56
0302-9743 s...The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding..978-3-030-73972-0978-3-030-73973-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 開(kāi)頭 時(shí)間: 2025-3-28 16:29
0302-9743 n, S+SSPR 2020, held in Padua, Italy, in January 2021...The 35 papers presented in this volume were carefully reviewed and selected from 81 submissions...The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, str作者: dermatomyositis 時(shí)間: 2025-3-28 20:59
Experimental Analysis of Bidirectional Pairwise Ordinal Classifier Cascadesevaluate our proposed approach based on different OC benchmark data sets. Additionally, we analyse the proposed bOCCs in combination with two different classification models. Our outcomes indicate that it seems to be beneficial to replace basic pairwise one-directional OCCs by the pairwise bOCC architecture, in general.作者: Noisome 時(shí)間: 2025-3-28 23:53 作者: 滴注 時(shí)間: 2025-3-29 03:35
Predicting Polypharmacy Side Effects Through a Relation-Wise Graph Attention Network Attention Network (GAT), used to assign different weight to the different relationships in the multilayer graph. We experimentally demonstrate that the proposed GCN, compared with other recent methods, is able to achieve a state-of-the-art performance on a publicly available polypharmacy side effect network.作者: RACE 時(shí)間: 2025-3-29 10:06 作者: 颶風(fēng) 時(shí)間: 2025-3-29 11:29 作者: apropos 時(shí)間: 2025-3-29 16:06
Wouter M. Kouw,Marco Loogungen, da? wir in jedem Augenblick die M?glichkeit haben, so oder so zu handeln, klug oder t?richt, gut oder schlecht. Wie reimt sich dies beides zusammen? Sicherlich ist doch jeder einzelne von uns auch nur ein Stück der gro?en Welt, und daher ebenso wie alle übrigen Wesen ihren Gesetzen unterworfe作者: macrophage 時(shí)間: 2025-3-29 23:35
Maximilian Münch,Michiel Straat,Michael Biehl,Frank-Michael Schleifungen, da? wir in jedem Augenblick die M?glichkeit haben, so oder so zu handeln, klug oder t?richt, gut oder schlecht. Wie reimt sich dies beides zusammen? Sicherlich ist doch jeder einzelne von uns auch nur ein Stück der gro?en Welt, und daher ebenso wie alle übrigen Wesen ihren Gesetzen unterworfe作者: 領(lǐng)帶 時(shí)間: 2025-3-30 02:21 作者: RECUR 時(shí)間: 2025-3-30 05:07
Boyuan Wang,Lixin Cui,Lu Bai,Edwin R. Hancockbis zur Gew?hrleistung und kann den firmenspezifischen Bedürfnissen angepa?t werden..Mit dem firmenspezifisch überarbeiteten QM-Handbuch steht Baubetrieben ein wirksames Hilfsmittel zur Verfügung, Kunden bzw. Auftraggebern die Qualit?t des Unternehmens zu bescheinigen sowie durch klare Festlegungen 作者: 憤慨點(diǎn)吧 時(shí)間: 2025-3-30 12:02 作者: 誘拐 時(shí)間: 2025-3-30 14:59 作者: Ophthalmoscope 時(shí)間: 2025-3-30 16:54 作者: Spirometry 時(shí)間: 2025-3-30 23:32 作者: 古代 時(shí)間: 2025-3-31 02:14 作者: 背叛者 時(shí)間: 2025-3-31 05:46 作者: Excise 時(shí)間: 2025-3-31 11:19
Vincenzo Carletti,Pasquale Foggia,Antonio Greco,Antonio Roberto,Mario Vento
