作者: Conflict 時間: 2025-3-21 21:05 作者: Needlework 時間: 2025-3-22 01:43
Towards Model-Based Reinforcement Learning for Industry-Near Environmentslittle visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have shown outstanding performance in a variety of tasks, including Atari 2600, MuJoCo, and Roboschool test suite. Although these algorithms are fundamentally different, both suffer from high variance, low sample efficien作者: SHRIK 時間: 2025-3-22 05:29
Stepwise Evolutionary Learning Using Deep Learned Guidance Functions evolutionary algorithms. A new form of LGF is introduced, based on deep neural network learning, and it is shown how this can be used as a fitness function. This is applied to a test problem: unscrambling the Rubik’s Cube. Comparisons are made with a previous LGF approach based on random forests, a作者: 極肥胖 時間: 2025-3-22 08:44
Monotonicity Detection and Enforcement in Longitudinal Classificationeful knowledge based on the changes of the data over time. Monotonic relations often occur in real-world data and need to be preserved in data mining models in order for the models to be acceptable by users. We propose a new methodology for detecting monotonic relations in longitudinal datasets and 作者: liposuction 時間: 2025-3-22 16:17
Understanding Structure of Concurrent Actionsre in the action space can improve sample efficiency during exploration. To show this we focus on concurrent action spaces where the RL agent selects multiple actions per timestep. Concurrent action spaces are challenging to learn in especially if the number of actions is large as this can lead to a作者: 功多汁水 時間: 2025-3-22 19:01 作者: anthesis 時間: 2025-3-22 23:17
Ontology-Driven, Adaptive, Medical Questionnaires for Patients with Mild Learning Disabilities inequalities and worse outcomes than the general population. Primary care practitioners are often the first port-of-call for medical consultations, and one issue faced by LD patients in this context is the very limited time available during consultations - typically less than ten minutes. In order 作者: flammable 時間: 2025-3-23 05:05 作者: 含鐵 時間: 2025-3-23 08:22 作者: 無節(jié)奏 時間: 2025-3-23 11:55 作者: ILEUM 時間: 2025-3-23 16:24
Building Knowledge Intensive Architectures for Heterogeneous NLP Workflowse being used increasingly due to their flexibility in a working environment where they minimize mundane tasks like long-term maintenance and increase productivity by automatically responding to changes and introducing new processes. Constant changes within unstable environments where information may作者: Integrate 時間: 2025-3-23 21:14
WVD: A New Synthetic Dataset for Video-Based Violence Detectionisting datasets and methods discriminate between violent and non-violent scenes based on very abstract definitions of violence. Available datasets, such as “Hockey Fight” and “Movies”, only contain fight versus non-fight videos; no weapons are discriminated in them. In this paper, we focus explicitl作者: infelicitous 時間: 2025-3-23 22:53 作者: 悲痛 時間: 2025-3-24 05:57 作者: 自負(fù)的人 時間: 2025-3-24 06:53
Metagaming and Paratextual Playnction. This is applied to a test problem: unscrambling the Rubik’s Cube. Comparisons are made with a previous LGF approach based on random forests, and with a baseline approach based on traditional error-based fitness.作者: 其他 時間: 2025-3-24 10:47 作者: ALLAY 時間: 2025-3-24 17:12
https://doi.org/10.1007/978-3-030-59908-9limination using task-invariant actions; a second approach looks for more explicit structure in the form of action clusters. Both methods are context-free, focusing only on an analysis of the action space and show a significant improvement in policy convergence times.作者: MERIT 時間: 2025-3-24 21:56
https://doi.org/10.1007/978-0-387-21840-3s through aggregated results and wordcloud visualizations. The effectiveness of our approach is finally evaluated through the use of commonly used datasets and compared in line with existing research.作者: enlist 時間: 2025-3-25 02:48 作者: 的事物 時間: 2025-3-25 06:42
Exposing Knowledge: Providing a Real-Time View of the Domain Under Study for Studentss through aggregated results and wordcloud visualizations. The effectiveness of our approach is finally evaluated through the use of commonly used datasets and compared in line with existing research.作者: 討厭 時間: 2025-3-25 09:25 作者: Locale 時間: 2025-3-25 15:04
Interactive Music Applications and Standardsport reasoning in hybrid domains. In particular, we propose an architecture to integrate Model Predictive Control (MPC) techniques from the field of control systems into an automated planner, to guide the effective exploration of the search space.作者: 懶鬼才會衰弱 時間: 2025-3-25 17:23 作者: 一大群 時間: 2025-3-25 21:07 作者: 不透明 時間: 2025-3-26 00:13
0302-9743 : machine learning; knowledge discovery and data mining; agents, knowledge acquisition and ontologies; medical applications; applications of evolutionary algorithms; machine learning for time series data; applications of machine learning; and knowledge acquisition.978-3-030-34884-7978-3-030-34885-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: jungle 時間: 2025-3-26 05:08 作者: CAMP 時間: 2025-3-26 11:44
A Decidable Analysis of Security Protocolsg involved in applying the MTM to new problems. Further, we demonstrate empirically that the MTM provides similar performance to what is achieved with a finely specificity-optimized TM, by comparing their performance on both synthetic and real-world datasets.作者: Eeg332 時間: 2025-3-26 13:09
Jim E. H. Bright,Robert G. L. Pryorure selection. We compare our results with the results from ad hoc parameter settings of the model from previous work and show that the combined genetic algorithm and neural network based approach further improves forecasting accuracy which helps service stations better manage their resource requirements.作者: defuse 時間: 2025-3-26 18:50 作者: scrape 時間: 2025-3-26 20:57 作者: NAIVE 時間: 2025-3-27 03:52 作者: 諂媚于人 時間: 2025-3-27 08:29
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-labelt and requires minimal hyperparameter tuning. The performance of the CasecadeML approach is evaluated using 10 multi-label datasets and compared with other leading multi-label classification algorithms. Results show that CascadeML performs comparatively with the leading approaches but without a need作者: 細(xì)節(jié) 時間: 2025-3-27 11:18
Towards Model-Based Reinforcement Learning for Industry-Near Environmentsl-based reinforcement are ideal for real-world environments where sampling is slow and in mission-critical operations. In the warehouse industry, there is an increasing motivation to minimise time and to maximise production. In many of these environments, the literature suggests that the autonomous 作者: BAIT 時間: 2025-3-27 15:27
Monotonicity Detection and Enforcement in Longitudinal Classificationwo scenarios: enforcing and not enforcing the constraints. The results show that enforcement of monotonicity constraints can consistently improve the predictive accuracy of the constructed models. The produced models are fully monotonic according to the monotonicity constraints, which can have a pos作者: 浮夸 時間: 2025-3-27 21:03
Demonstrating the Distinctions Between Persuasion and Deliberation Dialoguesvide the design and implementation details of our new tool along with an evaluation of the software. The tool we have produced captures the distinctive features of each of the two dialogue types, to make plain their differences and to validate the speech acts for use in practical scenarios.作者: 牽連 時間: 2025-3-28 00:49
Ontology-Driven, Adaptive, Medical Questionnaires for Patients with Mild Learning Disabilitiesh carried out in the development of adaptive medical questionnaires to include interactive and interface functionalities designed specifically to cater for patients with potentially complex accessibility needs. A patient’s current health status and accessibility profile (relating to their impairment作者: 思考才皺眉 時間: 2025-3-28 02:44 作者: Default 時間: 2025-3-28 09:04 作者: Infraction 時間: 2025-3-28 12:41 作者: 發(fā)酵劑 時間: 2025-3-28 15:26 作者: Chandelier 時間: 2025-3-28 20:55
https://doi.org/10.1007/978-3-030-59908-9wo scenarios: enforcing and not enforcing the constraints. The results show that enforcement of monotonicity constraints can consistently improve the predictive accuracy of the constructed models. The produced models are fully monotonic according to the monotonicity constraints, which can have a pos作者: 無孔 時間: 2025-3-29 02:57
https://doi.org/10.1007/978-3-030-53571-1vide the design and implementation details of our new tool along with an evaluation of the software. The tool we have produced captures the distinctive features of each of the two dialogue types, to make plain their differences and to validate the speech acts for use in practical scenarios.作者: Obedient 時間: 2025-3-29 04:11 作者: CLOWN 時間: 2025-3-29 10:27
https://doi.org/10.1007/978-3-031-66395-6efine a custom prioritization strategy, to achieve the best possible result. This paper describes a novel workflow architecture for heavy knowledge-related application workflows to address the tasks of high solution accuracy and shorter prediction resolution time. We describe how policies can be gen作者: PLAYS 時間: 2025-3-29 12:28
https://doi.org/10.1007/978-3-031-66395-6 To the best of our knowledge no similar dataset, that captures weapon-based violence, exists. The paper evaluates the proposed dataset by utilising local feature descriptors using an SVM classifier. The extracted features are aggregated using the Bag of Visual Word (BoVW) technique to classify weap作者: 敬禮 時間: 2025-3-29 17:28
Artificial Intelligence XXXVI978-3-030-34885-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Vulnerary 時間: 2025-3-29 20:59
https://doi.org/10.1007/978-3-319-00681-9sification is to train individual binary classifiers per label, but the performance can be improved by considering associations between the labels, and algorithms like classifier chains and RAKEL do this effectively. Like most machine learning algorithms, however, these approaches require accurate h作者: Trypsin 時間: 2025-3-30 00:59 作者: 休閑 時間: 2025-3-30 07:05 作者: figment 時間: 2025-3-30 09:48 作者: 項(xiàng)目 時間: 2025-3-30 14:45 作者: 燒瓶 時間: 2025-3-30 16:40
https://doi.org/10.1007/978-3-030-59908-9re in the action space can improve sample efficiency during exploration. To show this we focus on concurrent action spaces where the RL agent selects multiple actions per timestep. Concurrent action spaces are challenging to learn in especially if the number of actions is large as this can lead to a作者: 門閂 時間: 2025-3-30 22:35
