標題: Titlebook: Belief Functions: Theory and Applications; 8th International Co Yaxin Bi,Anne-Laure Jousselme,Thierry Denoeux Conference proceedings 2024 T [打印本頁] 作者: 桌前不可入 時間: 2025-3-21 20:01
書目名稱Belief Functions: Theory and Applications影響因子(影響力)
書目名稱Belief Functions: Theory and Applications影響因子(影響力)學科排名
書目名稱Belief Functions: Theory and Applications網(wǎng)絡(luò)公開度
書目名稱Belief Functions: Theory and Applications網(wǎng)絡(luò)公開度學科排名
書目名稱Belief Functions: Theory and Applications被引頻次
書目名稱Belief Functions: Theory and Applications被引頻次學科排名
書目名稱Belief Functions: Theory and Applications年度引用
書目名稱Belief Functions: Theory and Applications年度引用學科排名
書目名稱Belief Functions: Theory and Applications讀者反饋
書目名稱Belief Functions: Theory and Applications讀者反饋學科排名
作者: 乏味 時間: 2025-3-21 22:31 作者: 討厭 時間: 2025-3-22 03:24 作者: ANA 時間: 2025-3-22 08:11
Conflict Management in?a?Distance to?Prototype-Based Evidential Deep LearningThe experimental results are obtained using a lidar-camera cross-fusion architecture with evidential formulation based on Dempster-Shafer’s theory. The model is investigated on a road detection task and it uses the KITTI dataset.作者: affluent 時間: 2025-3-22 09:07
https://doi.org/10.1007/978-3-540-44760-3kes place currently. For voluminous datasets, users can also adjust the update frequency according to their specific requirements. Compared to state-of-the-art (SoTA) stream clustering algorithms, IEC demonstrates better clustering accuracy and comparable runtime across four benchmark datasets. IEC 作者: Conquest 時間: 2025-3-22 13:40 作者: 共同時代 時間: 2025-3-22 19:04
1 Deformation behaviour of steel,pproach presented in the work [.]. Hence, this enables us to detect hallucinations by evaluating the conflict among evidence. Preliminary experiments were conducted on a state-of-the-art LVLM, mPLUG-Owl2. Results show that our approach exhibits an enhancement over baseline methods, particularly in c作者: LATER 時間: 2025-3-22 21:48 作者: zonules 時間: 2025-3-23 02:41
An Evidential Time-to-Event Prediction Model Based on?Gaussian Random Fuzzy Numbersdel is fit by minimizing a generalized negative log-likelihood function that accounts for both normal and censored data. Comparative experiments on two real-world datasets demonstrate the very good performance of our model as compared to the state-of-the-art.作者: BAIL 時間: 2025-3-23 07:49 作者: VAN 時間: 2025-3-23 12:18
Decision Theory via?Model-Free Generalized Fiducial Inferenceecision theory, our work builds on these connections. In our paper, we establish pointwise and uniform consistency of an . as an approximation to the true risk function via the derivation of nonasymptotic concentration bounds, and our work serves as the foundation for future investigations of the properties of the MFGF upper risk.作者: MORPH 時間: 2025-3-23 17:02
0302-9743 n September 2–4, 2024...The 30 full papers presented in this book were carefully selected and reviewed from 36 submissions. The papers cover a wide range on theoretical aspects on?Machine learning;?Statistical inference;?Information fusion and optimization;?Measures of uncertainty, conflict and dist作者: 除草劑 時間: 2025-3-23 20:34
Steel symbol/number: DD11/1.0332, uncertainty is described by a random fuzzy set inducing a predictive belief function. Preliminary experiments suggest that the approximations are very accurate and that the method allows for conservative uncertainty-aware predictions.作者: Glaci冰 時間: 2025-3-23 23:22 作者: Inexorable 時間: 2025-3-24 05:39 作者: novelty 時間: 2025-3-24 08:38 作者: micturition 時間: 2025-3-24 13:46 作者: 人充滿活力 時間: 2025-3-24 17:09 作者: 臆斷 時間: 2025-3-24 19:47 作者: 雜色 時間: 2025-3-25 00:58 作者: 極小量 時間: 2025-3-25 05:28
Conference proceedings 2024r 2–4, 2024...The 30 full papers presented in this book were carefully selected and reviewed from 36 submissions. The papers cover a wide range on theoretical aspects on?Machine learning;?Statistical inference;?Information fusion and optimization;?Measures of uncertainty, conflict and distances;?Con作者: 勤勉 時間: 2025-3-25 09:54
4.5 Thermal expansion of steel,lgorithms, and other state-of-the-art techniques. DEEM can learn from the data itself, without requiring external labels but we can incorporate prior on labels if available as proposed in NN-EVCLUS. The first results are shown on the MNIST dataset (digit recognition).作者: 柔聲地說 時間: 2025-3-25 13:48
https://doi.org/10.1007/978-3-540-44760-3 classification criteria. We conclude by presenting some preliminary experimental results, demonstrating the performance of the proposed models compared to commonly used probabilistic circuits across a range of classification tasks.作者: 爭議的蘋果 時間: 2025-3-25 18:12 作者: companion 時間: 2025-3-25 23:40 作者: 吹牛大王 時間: 2025-3-26 04:11
Deep Evidential Clustering of?Imageslgorithms, and other state-of-the-art techniques. DEEM can learn from the data itself, without requiring external labels but we can incorporate prior on labels if available as proposed in NN-EVCLUS. The first results are shown on the MNIST dataset (digit recognition).作者: cathartic 時間: 2025-3-26 07:24 作者: 爆米花 時間: 2025-3-26 11:52
An Evidence-Based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Pzes multi-sourced encoders to address the heterogeneity in EHRs and combines the multi-sourced evidence using Dempster’s combination rule. Our framework significantly outperforms conventional EHR analysis methods, demonstrating higher effectiveness on two tabular encoders in mortality prediction.作者: neutrophils 時間: 2025-3-26 13:28
A Novel Privacy Preserving Framework for?Training Dempster-Shafer Theory-Based Evidential Deep Neurats with the CIFAR10 and CIFAR100 datasets confirm that integrating SMC with FL in DS-based EDNNs preserves high classification accuracy while effectively handling unclear patterns, ensuring advanced decision-making and robust data privacy and security.作者: chronology 時間: 2025-3-26 18:27
0302-9743 nge on theoretical aspects on?Machine learning;?Statistical inference;?Information fusion and optimization;?Measures of uncertainty, conflict and distances;?Continuous belief functions, logics, computation..978-3-031-67976-6978-3-031-67977-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 尖牙 時間: 2025-3-27 01:02
Steel symbol/number: DC04/1.0338,del is fit by minimizing a generalized negative log-likelihood function that accounts for both normal and censored data. Comparative experiments on two real-world datasets demonstrate the very good performance of our model as compared to the state-of-the-art.作者: 不溶解 時間: 2025-3-27 03:55 作者: BLANK 時間: 2025-3-27 07:04
Steel symbol/number: DC04/1.0338,ecision theory, our work builds on these connections. In our paper, we establish pointwise and uniform consistency of an . as an approximation to the true risk function via the derivation of nonasymptotic concentration bounds, and our work serves as the foundation for future investigations of the properties of the MFGF upper risk.作者: Apraxia 時間: 2025-3-27 09:50 作者: 使人煩燥 時間: 2025-3-27 16:24 作者: 軌道 時間: 2025-3-27 18:15 作者: GLOOM 時間: 2025-3-28 01:11
Incremental Belief-Peaks Evidential Clusteringation in the realm of big data remains constrained by excessive computational complexity and limited computational resources. To bridge this research gap, this paper introduces an .ncremental .vidential .lustering (IEC) method based on stream data clustering and belief-peaks, a technique that has de作者: Prophylaxis 時間: 2025-3-28 02:30 作者: AMEND 時間: 2025-3-28 09:55
Dempster-Shafer Credal Probabilistic Circuitsications do not fully account for epistemic uncertainty. To address this, credal probabilistic circuits were introduced, incorporating a way to manage such uncertainty. We propose a novel framework for learning the structure and parameters of credal probabilistic circuits, leveraging the Dempster-Sh作者: 非實體 時間: 2025-3-28 12:44
Uncertainty Quantification in?Regression Neural Networks Using Likelihood-Based Belief Functions is based on the Gaussian approximation of the likelihood function and the linearization of the network output with respect to the weights. Prediction uncertainty is described by a random fuzzy set inducing a predictive belief function. Preliminary experiments suggest that the approximations are ver作者: 合適 時間: 2025-3-28 15:12
An Evidential Time-to-Event Prediction Model Based on?Gaussian Random Fuzzy Numbersandom fuzzy numbers, a newly introduced family of random fuzzy subsets of the real line with associated belief functions, generalizing both Gaussian random variables and Gaussian possibility distributions. Our approach makes minimal assumptions about the underlying time-to-event distribution. The mo作者: refraction 時間: 2025-3-28 19:07
Object Hallucination Detection in?Large Vision Language Models via?Evidential Conflict. This is particularly presented as the object hallucination, where the models inaccurately describe objects in the images. Current efforts mainly focus on detecting such erroneous behaviors through the semantic consistency of outputs via multiple inferences or by evaluating the entropy-based uncert作者: EVADE 時間: 2025-3-29 00:18
Multi-oversampling with?Evidence Fusion for?Imbalanced Data Classificationt oversampling methods overlook the uncertainty in the samples produced, potentially shifting the data’s distribution and adversely affecting the classification outcomes. To address this problem, we introduce a multi-oversampling with evidence fusion (MOEF) method for imbalanced data classification 作者: 不能強迫我 時間: 2025-3-29 03:47 作者: legacy 時間: 2025-3-29 07:46
Conflict Management in?a?Distance to?Prototype-Based Evidential Deep Learningent of perception models. If the pieces of evidence involved in the merging process of the deep learning-based model are discordant, the results can be degraded. Therefore, verifying the conflicting level of sources and alleviating it when possible, gives the capability to increase efficiency of fol作者: mosque 時間: 2025-3-29 15:10 作者: ARK 時間: 2025-3-29 19:06 作者: 凝視 時間: 2025-3-29 20:41
Variational Approximations of?Possibilistic Inferential Modelsient computation in applications is a major challenge. This paper presents a simple and apparently powerful Monte Carlo-driven strategy for approximating the IM’s possibility contour, or at least its .-level set for a specified .. Our proposal utilizes a parametric family that, in a certain sense, a作者: Medicare 時間: 2025-3-30 03:04 作者: 動物 時間: 2025-3-30 06:56
Which Statistical Hypotheses are Afflicted with?False Confidence?ivial and non-trivial) false hypotheses to which the method tends to assign high confidence. This raises concerns about the reliability of these widely-used methods, and shines promising light on the consonant belief function-based methods that are provably immune to false confidence. But an existen作者: maverick 時間: 2025-3-30 10:51 作者: Nostalgia 時間: 2025-3-30 13:19
Conference proceedings 2024r 2–4, 2024...The 30 full papers presented in this book were carefully selected and reviewed from 36 submissions. The papers cover a wide range on theoretical aspects on?Machine learning;?Statistical inference;?Information fusion and optimization;?Measures of uncertainty, conflict and distances;?Continuous belief functions, logics, computation..作者: HUSH 時間: 2025-3-30 18:51 作者: addition 時間: 2025-3-30 20:48 作者: Enthralling 時間: 2025-3-31 03:44
4.5 Thermal expansion of steel,rtainty between clusters. The algorithm learns to generate mass functions for a given image through a training process that minimises a loss between the conflict computed from pairs of images and their dissimilarities. DEEM extends NN-EVCLUS and provides a gateway to the entire realm of deep learnin作者: Assault 時間: 2025-3-31 05:08 作者: Mucosa 時間: 2025-3-31 10:30 作者: packet 時間: 2025-3-31 14:50
