作者: 掃興 時(shí)間: 2025-3-21 23:33
,Regularization in?Probabilistic Inductive Logic Programming,erform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning via Expectation Maximization and LBFGS. In this paper, we present an updated version of LIFTCOVER, call作者: Anticlimax 時(shí)間: 2025-3-22 02:49
Towards ILP-Based , Passive Learning,inatorial nature of the problem, current state-of-the-art solutions are based on exhaustive search. They use an example at the time to discard a single candidate formula at the time, instead of exploiting the full set of examples to prune the search space. This hinders their applicability when examp作者: 陶醉 時(shí)間: 2025-3-22 05:00 作者: BLANC 時(shí)間: 2025-3-22 10:02
,Select First, Transfer Later: Choosing Proper Datasets for?Statistical Relational Transfer Learningtional and rich probability structures. Although SRL techniques have succeeded in many real-world applications, they follow the same assumption as most ML techniques by assuming training and testing data have the same distribution and are sampled from the same feature space. Changes between these di作者: 被告 時(shí)間: 2025-3-22 14:12
,GNN Based Extraction of?Minimal Unsatisfiable Subsets,atisfiability which, as a result, have been used in various applications. Although various systematic algorithms for the extraction of MUSes have been proposed, few heuristic methods have been studied, as the process of designing efficient heuristics requires extensive experience and expertise. In t作者: 膽小懦夫 時(shí)間: 2025-3-22 17:05 作者: 和音 時(shí)間: 2025-3-23 00:18 作者: 知識(shí)分子 時(shí)間: 2025-3-23 02:41
,An Experimental Overview of?Neural-Symbolic Systems,, more and more researchers have encountered the limitations of deep learning, which has led to a rise in the popularity of neural-symbolic AI, with a wide variety of systems being developed. However, many of these systems are either evaluated on different benchmarks, or introduce new benchmarks tha作者: CAGE 時(shí)間: 2025-3-23 08:19 作者: 吝嗇性 時(shí)間: 2025-3-23 11:45
,Meta-interpretive Learning from?Fractal Images, patterns at ever-smaller scales. This study offers a technique for learning from fractal images using Meta-Interpretative Learning (MIL). MIL has previously been employed for few-shot learning from geometrical shapes (e.g. regular polygons) and has exhibited significantly higher accuracy when compa作者: fluffy 時(shí)間: 2025-3-23 16:40
wide range of devices and applications. These conditions give rise to heterogeneous traffic offered to the network. In order to carry such traffic in a wireless network, the design and development of schedulers capable of considering the conditions of each user is needed. In this chapter a Model Ba作者: 職業(yè)拳擊手 時(shí)間: 2025-3-23 19:08 作者: 極為憤怒 時(shí)間: 2025-3-23 23:39 作者: circumvent 時(shí)間: 2025-3-24 06:25 作者: adduction 時(shí)間: 2025-3-24 09:43
Takeru Isobe,Katsumi Inoued their environment, the procurement costs could be a challenging issue for humanitarian organizations (HO). Post-disaster procurement and pre-positioning inventory usually carry large costs due to the haste of the situation in the first or the extensive administration in the latter. This work analy作者: 食物 時(shí)間: 2025-3-24 14:08 作者: perjury 時(shí)間: 2025-3-24 18:25
Sota Moriyama,Koji Watanabe,Katsumi Inouepending on the nature of the parameters to optimise. In the class of combinatorial problems, we find a sub-category which is the binary optimisation problems. Due to the complex nature of optimisation problems, exhaustive search-based methods are no longer a good choice. So, metaheuristics are more 作者: Minatory 時(shí)間: 2025-3-24 21:15
Kilian Rückschlo?,Felix Weitk?mpercognition and real time video processing methods. The performance of these algorithms have been severely hampered by their high intensive computation since the new video standards, especially those in high definitions require more resources and memory to achieve their computations. In this paper, we作者: MORPH 時(shí)間: 2025-3-25 01:15 作者: BLAZE 時(shí)間: 2025-3-25 06:46
Arne Vermeulen,Robin Manhaeve,Giuseppe Marracognition and real time video processing methods. The performance of these algorithms have been severely hampered by their high intensive computation since the new video standards, especially those in high definitions require more resources and memory to achieve their computations. In this paper, we作者: deforestation 時(shí)間: 2025-3-25 07:53
Felix Weitk?mper,Dmitriy Ravdin,Ramona Fabrypending on the nature of the parameters to optimise. In the class of combinatorial problems, we find a sub-category which is the binary optimisation problems. Due to the complex nature of optimisation problems, exhaustive search-based methods are no longer a good choice. So, metaheuristics are more 作者: calamity 時(shí)間: 2025-3-25 13:11
Youssef Mahmoud Youssef,Martin E. Müllerere it is necessary to process huge amount of data. The efficiency of existing feature selection algorithms significantly downgrades, if not totally inapplicable, when data size exceeds hundreds of gigabytes, because most feature selection algorithms are designed for centralized computing architectu作者: 安心地散步 時(shí)間: 2025-3-25 16:35
Daniel Cyrus,James Trewern,Alireza Tamaddoni-Nezhadures that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for face recognition and classification using a system based on WPD, fractal codes and two-dimensional subspace for feature extraction, and Combined Learning Vector Quantization an作者: LUMEN 時(shí)間: 2025-3-25 20:24 作者: 嬉耍 時(shí)間: 2025-3-26 01:21 作者: invert 時(shí)間: 2025-3-26 05:15
,GNN Based Extraction of?Minimal Unsatisfiable Subsets, process more efficient. We conduct experiments to compare our proposed method with existing methods on the MUS Track of the 2011 SAT Competition. From the results, NeuroMUSX is shown to achieve significantly better performance across a wide range of problem instances. In addition, training NeuroMUS作者: Mnemonics 時(shí)間: 2025-3-26 09:00
,Few-Shot Learning of?Diagnostic Rules for?Neurodegenerative Diseases Using Inductive Logic Programmdiseases using fundus images collected from the UK Biobank dataset. The logical representation and reasoning inherent in ILP enhances the interpretability of the detection process. The results highlight the efficacy of ILP in few-shot learning scenarios, showcasing its remarkable generalisation perf作者: ADOPT 時(shí)間: 2025-3-26 13:14 作者: Detonate 時(shí)間: 2025-3-26 19:57
,Statistical Relational Structure Learning with?Scaled Weight Parameters,time for large domains with minor performance trade-offs, which decrease with the size of the original domain. Additionally, the study explores how scaling reacts to varying domain sizes in a synthetic social network domain. It is observed that DA-MLNs outperform unscaled MLNs when the number of con作者: larder 時(shí)間: 2025-3-26 22:02
Elena Bellodi,Francesca Alessandra Lisi,Riccardo Z作者: 誹謗 時(shí)間: 2025-3-27 02:48
terms of throughput and fairness) through the evaluation of Maximum Rate (MR), Round Robin (RR), Proportional Fair (PF) and a proposed novel UE-based Maximum Rate (UEMR) scheduling algorithms are presented. Using MBD together with MBT the developed scheduling algorithms can be further enhanced and 作者: Freeze 時(shí)間: 2025-3-27 07:22 作者: 松軟 時(shí)間: 2025-3-27 09:54
Elisabetta Gentili,Alice Bizzarri,Damiano Azzolini,Riccardo Zese,Fabrizio Riguzzi purchase some reserved capacity from suppliers during a given period of time (also called horizon) so that better prices could be negotiated and help the organization lower their procurement costs..The proposed model delivers an optimal solution for a statutory agency and helps in the decision-maki作者: 地名表 時(shí)間: 2025-3-27 16:44 作者: 急急忙忙 時(shí)間: 2025-3-27 21:27 作者: chandel 時(shí)間: 2025-3-28 00:18
Thais Luca,Aline Paes,Gerson Zaverucha purchase some reserved capacity from suppliers during a given period of time (also called horizon) so that better prices could be negotiated and help the organization lower their procurement costs..The proposed model delivers an optimal solution for a statutory agency and helps in the decision-maki作者: 譏笑 時(shí)間: 2025-3-28 04:13
Sota Moriyama,Koji Watanabe,Katsumi Inoue In this paper, the efficiency of the principal mapping functions existing in the literature is investigated through the proposition of five binary variants of one of the most recent metaheuristic called the Bat Algorithm (BA). The proposed binary variants are evaluated on the APP, and have been tes作者: Constituent 時(shí)間: 2025-3-28 06:55 作者: decode 時(shí)間: 2025-3-28 14:05 作者: COM 時(shí)間: 2025-3-28 16:18 作者: 滲透 時(shí)間: 2025-3-28 21:40
Felix Weitk?mper,Dmitriy Ravdin,Ramona Fabry In this paper, the efficiency of the principal mapping functions existing in the literature is investigated through the proposition of five binary variants of one of the most recent metaheuristic called the Bat Algorithm (BA). The proposed binary variants are evaluated on the APP, and have been tes作者: abysmal 時(shí)間: 2025-3-28 23:56 作者: 豎琴 時(shí)間: 2025-3-29 05:58
Daniel Cyrus,James Trewern,Alireza Tamaddoni-Nezhadists of five parameters such as corresponding domain coordinates for each range block. Brightness offset and an affine transformation. The proposed approach is tested on ORL and FEI face databases. Experimental results on this database demonstrated the effectiveness of the proposed approach for face作者: sed-rate 時(shí)間: 2025-3-29 10:57 作者: 放牧 時(shí)間: 2025-3-29 13:39
978-3-031-49298-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Banister 時(shí)間: 2025-3-29 19:19 作者: 躺下殘殺 時(shí)間: 2025-3-29 21:59 作者: 隱語(yǔ) 時(shí)間: 2025-3-30 02:05
https://doi.org/10.1007/978-3-031-49299-0Counterfactual Reasoning; Probabilistic Logic Programming; (Probabilistic) Answer Set Programming; Meta作者: 令人不快 時(shí)間: 2025-3-30 06:26
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/i/image/463902.jpg作者: Jargon 時(shí)間: 2025-3-30 11:45
Conference proceedings 2023ring?November 13–15, 2023..The 11 full papers and 1 short paper included in this book were carefully reviewed and?selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-pr作者: 完成才會(huì)征服 時(shí)間: 2025-3-30 15:30
,Regularization in?Probabilistic Inductive Logic Programming,ER+ on the same 12 datasets on which LIFTCOVER was tested and compared the performances in terms of AUC-ROC, AUC-PR, and execution times. Results show that in most cases Expectation Maximization with regularization improves the quality of the solutions.
