標(biāo)題: Titlebook: Discovery Science; 25th International C Poncelet Pascal,Dino Ienco Conference proceedings 2022 The Editor(s) (if applicable) and The Author [打印本頁(yè)] 作者: CHORD 時(shí)間: 2025-3-21 18:19
書目名稱Discovery Science影響因子(影響力)
書目名稱Discovery Science影響因子(影響力)學(xué)科排名
書目名稱Discovery Science網(wǎng)絡(luò)公開度
書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Discovery Science被引頻次
書目名稱Discovery Science被引頻次學(xué)科排名
書目名稱Discovery Science年度引用
書目名稱Discovery Science年度引用學(xué)科排名
書目名稱Discovery Science讀者反饋
書目名稱Discovery Science讀者反饋學(xué)科排名
作者: Vo2-Max 時(shí)間: 2025-3-21 23:57 作者: 令人心醉 時(shí)間: 2025-3-22 02:45
Vergleichende Au?en- und Sicherheitspolitikstructures to summarize the information contained in the training examples, which can be quickly updated and allows to retrieve the best rule for each incoming example. The behavior of i. is evaluated with different parameterizations, and compared to other best-known incremental symbolic learning algorithms such as . and ..作者: Induction 時(shí)間: 2025-3-22 05:51 作者: 得體 時(shí)間: 2025-3-22 11:46 作者: 延期 時(shí)間: 2025-3-22 15:18 作者: 延期 時(shí)間: 2025-3-22 18:57
Studienerfolg und Studienabbruchnd analyze two strategies for conducting policy evaluation under cumulative periodic rewards, and study them by making use of simulation environments. Our findings indicate that both strategies can achieve similar sample efficiency as when we have consistent rewards.作者: 原來(lái) 時(shí)間: 2025-3-22 21:51 作者: 鐵砧 時(shí)間: 2025-3-23 05:14
Policy Evaluation with?Delayed, Aggregated Anonymous Feedbacknd analyze two strategies for conducting policy evaluation under cumulative periodic rewards, and study them by making use of simulation environments. Our findings indicate that both strategies can achieve similar sample efficiency as when we have consistent rewards.作者: hieroglyphic 時(shí)間: 2025-3-23 05:49 作者: interrogate 時(shí)間: 2025-3-23 12:50
https://doi.org/10.1007/978-3-658-19063-7ain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare作者: engender 時(shí)間: 2025-3-23 15:57 作者: 突變 時(shí)間: 2025-3-23 21:07
https://doi.org/10.1007/978-3-658-20287-3re investigated approaches is the use of a special type of quantum circuit – a so-called quantum neural network – to serve as a basis for a machine learning model. Roughly speaking, as the name suggests, a quantum neural network can play a similar role to a neural network. However, specifically for 作者: BRAVE 時(shí)間: 2025-3-23 22:27 作者: exophthalmos 時(shí)間: 2025-3-24 05:11 作者: 可憎 時(shí)間: 2025-3-24 10:36
Vergleichende Au?en- und Sicherheitspolitik fully supervised or completely unsupervised approaches. Supervised methods exploit labels to find change points that are as accurate as possible with respect to these labels, but have the drawback that annotating the data is a time-consuming task. In contrast, unsupervised methods avoid the need fo作者: 吸氣 時(shí)間: 2025-3-24 11:47
Studienbuch Politikwissenschaft domain incremental continual learning (OD-ICL), this distribution change happens in the input space without affecting the label distribution. In order to adapt to such changes, the model being trained risks forgetting previously learned knowledge (stability). On the other hand, enforcing that the m作者: fixed-joint 時(shí)間: 2025-3-24 17:53
Vergleichende Au?en- und Sicherheitspolitiksentations do not easily allow for gradual refinements of the learned concept. While the problem is less severe for incremental induction of decision trees, it is much harder for incremental rule learning in that there are hardly any incremental rule learning algorithms which are really successful. 作者: PANT 時(shí)間: 2025-3-24 19:38
Studienerfolg und Studienabbruchas they guide the agent towards its learning objective. However, having consistent rewards can be infeasible in certain scenarios, due to either cost, the nature of the problem or other constraints. In this paper, we investigate the problem of delayed, aggregated, and anonymous rewards. We propose a作者: 籠子 時(shí)間: 2025-3-25 01:31
Susanne Falk,Maximiliane Marschallarned predictive models. Most of this data is spatially auto-correlated, which violates the classical i.i.d. assumption (identically and independently distributed data) commonly used in machine learning. One of the largest challenges in relation to spatial auto-correlation is how to generate testing作者: muster 時(shí)間: 2025-3-25 05:52 作者: In-Situ 時(shí)間: 2025-3-25 10:39
Fachhochschule Ludwigshafen am Rheinve been proposed to generate compact similarity-preserving representations of time series, enabling real-time similarity search on large in-memory data collections. However, the existing techniques are not ideally suited for assessing similarity when sequences are locally out of phase. In this paper作者: Irksome 時(shí)間: 2025-3-25 12:07
Hochschule Pforzheim UniversityDeveloping robust methods for rapid and accurate stress detection plays an important role in improving people’s life quality and wellness. Prior research shows that analyzing physiological signals collected from wearable sensors is a reliable predictor of stress. For stress detection, methods based 作者: 生來(lái) 時(shí)間: 2025-3-25 16:02 作者: 態(tài)度暖昧 時(shí)間: 2025-3-25 20:09
0302-9743 October 10-12, 2022...The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions. ?.978-3-031-18839-8978-3-031-18840-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: objection 時(shí)間: 2025-3-26 04:03
Conference proceedings 2022.This book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12, 2022...The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions. ?.作者: 手段 時(shí)間: 2025-3-26 07:04 作者: 容易做 時(shí)間: 2025-3-26 08:45
978-3-031-18839-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Sedative 時(shí)間: 2025-3-26 15:33 作者: 藝術(shù) 時(shí)間: 2025-3-26 18:30
https://doi.org/10.1007/978-3-658-19063-7ression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used . as an objective function are practically better than the models produced by their respective standard boostin作者: coltish 時(shí)間: 2025-3-26 21:40
https://doi.org/10.1007/978-3-658-20287-3 this to 7 open-source datasets from the OpenML-CC18 classification benchmark whose number of features is small enough to fit on quantum hardware with less than 20 qubits. Three main levels of importance were detected from the ranking of hyperparameters obtained with functional ANOVA. Our experiment作者: Infraction 時(shí)間: 2025-3-27 02:22
Einleitung und Vorbemerkung der Herausgeber,ted AL runs on purely synthetic datasets. To show that . was successfully trained, we perform an extensive evaluation comparing our strategy on 13 different datasets, from a wide range of domains, with 7 other query strategies.作者: NAG 時(shí)間: 2025-3-27 06:59 作者: 直覺好 時(shí)間: 2025-3-27 10:06
Studienbuch Politikwissenschaft(TP) is trained to select the most suitable NN from the frozen pool for prediction. We compare ODIN against popular regularization and replay methods. It outperforms regularization methods and achieves comparable predictive performance to replay methods.作者: fatty-acids 時(shí)間: 2025-3-27 17:08 作者: 整理 時(shí)間: 2025-3-27 19:13
,Begriffskl?rung von Studienerfolg, temporal information related to historical measurements using multiple strategies, as well as that of simultaneously predicting multiple future consumption measurements in a multi-step predictive setting. Finally, we investigate the effectiveness of injecting descriptive features to make the learni作者: 粘 時(shí)間: 2025-3-27 22:51 作者: 定點(diǎn) 時(shí)間: 2025-3-28 05:38 作者: GEAR 時(shí)間: 2025-3-28 07:22
Die Hochschulstandorte im überblick predict the next activity of an ongoing process, when it will occur, and which resource it will trigger. Finally, we thoroughly evaluated these models in real datasets. In particular, we found that Multi-attribute Transformers can outperform Transformers that only use information about previous act作者: 脆弱吧 時(shí)間: 2025-3-28 13:16 作者: mutineer 時(shí)間: 2025-3-28 15:51
Hyperparameter Importance of?Quantum Neural Networks Across Small Datasets this to 7 open-source datasets from the OpenML-CC18 classification benchmark whose number of features is small enough to fit on quantum hardware with less than 20 qubits. Three main levels of importance were detected from the ranking of hyperparameters obtained with functional ANOVA. Our experiment作者: 無(wú)力更進(jìn) 時(shí)間: 2025-3-28 19:59 作者: Indent 時(shí)間: 2025-3-29 02:23 作者: gain631 時(shí)間: 2025-3-29 06:01
Adaptive Neural Networks for?Online Domain Incremental Continual Learning(TP) is trained to select the most suitable NN from the frozen pool for prediction. We compare ODIN against popular regularization and replay methods. It outperforms regularization methods and achieves comparable predictive performance to replay methods.作者: CROW 時(shí)間: 2025-3-29 10:30 作者: invert 時(shí)間: 2025-3-29 12:35
Leveraging Spatio-Temporal Autocorrelation to Improve the?Forecasting of?the Energy Consumption in S temporal information related to historical measurements using multiple strategies, as well as that of simultaneously predicting multiple future consumption measurements in a multi-step predictive setting. Finally, we investigate the effectiveness of injecting descriptive features to make the learni作者: tic-douloureux 時(shí)間: 2025-3-29 18:24
Elastic Product Quantization for?Time Seriesments, which we address with a pre-alignment step using the maximal overlap discrete wavelet transform (MODWT). To demonstrate the efficiency and accuracy of our method, we perform an extensive experimental evaluation on benchmark datasets in nearest neighbors classification and clustering applicati作者: 捐助 時(shí)間: 2025-3-29 20:25 作者: 紀(jì)念 時(shí)間: 2025-3-30 03:00 作者: UTTER 時(shí)間: 2025-3-30 04:35
Data-Driven Prediction of?Athletes’ Performance Based on?Their Social Media Presence作者: 夾死提手勢(shì) 時(shí)間: 2025-3-30 08:46
Model Optimization in?Imbalanced Regressionain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare作者: ADJ 時(shí)間: 2025-3-30 14:13
Discovery of?Differential Equations Using Probabilistic Grammarsic domains. The paper introduces a novel method for inferring ODEs from data. It extends ProGED, a method for equation discovery that employs probabilistic context-free grammars for constraining the space of candidate equations. The proposed method can discover ODEs from partial observations of dyna作者: maudtin 時(shí)間: 2025-3-30 18:50 作者: finite 時(shí)間: 2025-3-30 23:41
: Learned Active Learning Strategy on?Synthetic Datarmation based on a query strategy. In the past, a large variety of such query strategies has been proposed, with each generation of new strategies increasing the runtime and adding more complexity. However, to the best of our knowledge, none of these strategies excels consistently over a large numbe作者: PRO 時(shí)間: 2025-3-31 01:45 作者: 托運(yùn) 時(shí)間: 2025-3-31 08:48
Semi-supervised Change Point Detection Using Active Learning fully supervised or completely unsupervised approaches. Supervised methods exploit labels to find change points that are as accurate as possible with respect to these labels, but have the drawback that annotating the data is a time-consuming task. In contrast, unsupervised methods avoid the need fo作者: Myocarditis 時(shí)間: 2025-3-31 09:38
