標題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Hendrik Blockeel,Kristian Kersting,Filip ?elezny Conference pro [打印本頁] 作者: 誓約 時間: 2025-3-21 17:18
書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)
書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學科排名
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書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡公開度學科排名
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學科排名
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書目名稱Machine Learning and Knowledge Discovery in Databases年度引用學科排名
書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋
書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學科排名
作者: 焦慮 時間: 2025-3-21 23:44
Qifeng Qiao,Peter A. Belinggriff adaptive Regelung zusammengefa?t werden k?nnen (Tsypkin, 1973, Saridis, 1979, Astr?m et al., 1977). Sie werden insbesonder dann ben?tigt, wenn klassische mathematische Systeml?sungen zur Regelung des technischen Prozesses nicht angewandt werden k?nnen, da entweder ungenügendes Wissen bezüglich作者: 常到 時間: 2025-3-22 02:55 作者: NATAL 時間: 2025-3-22 06:51
José Bento,Stratis Ioannidis,S. Muthukrishnan,Jinyun Yanldung. Neuerdings wenden sich auch betriebliche Kursangebote an die ?Bildungsverlierer“ unseres Schulsystems, die mit z.T. massiven Problemen die Haupt- oder Sonderschule verlassen. Diese qualitative Studie ist dem Bereich der subjektwissenschaftlichen Lernforschung zuzuordnen. Sie rekonstruiert die作者: 范圍廣 時間: 2025-3-22 09:46 作者: 釋放 時間: 2025-3-22 13:41 作者: osteoclasts 時間: 2025-3-22 21:02 作者: 拋物線 時間: 2025-3-22 23:24
Recognition of Agents Based on Observation of Their Sequential Behaviorbehavior. We model the problem faced by the agents as a Markov decision process (MDP) and model the observed behavior of an agent in terms of forward planning for the MDP. The reality of the agent’s decision problem and process may not be expressed by the MDP and its policy, but we interpret the obs作者: STELL 時間: 2025-3-23 04:32
Learning Throttle Valve Control Using Policy Searchynamics and demanding constraints for the controller. Using state-of-the-art throttle valve control, such as model-free PID controllers, time-consuming and manual adjusting of the controller is necessary. In this paper, we investigate how reinforcement learning (RL) can help to alleviate the effort 作者: 強有力 時間: 2025-3-23 06:44 作者: Reverie 時間: 2025-3-23 12:08 作者: 令人悲傷 時間: 2025-3-23 15:14
Regret Bounds for Reinforcement Learning with Policy Advicevisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand. We prove that RLPA has a sub-linear regret of . relative to the best input policy, and that both th作者: 滔滔不絕的人 時間: 2025-3-23 18:28
Exploiting Multi-step Sample Trajectories for Approximate Value Iterationunction approximators used in such methods typically introduce errors in value estimation which can harm the quality of the learned value functions. We present a new batch-mode, off-policy, approximate value iteration algorithm called Trajectory Fitted Q-Iteration (TFQI). This approach uses the sequ作者: LIKEN 時間: 2025-3-23 23:23 作者: animated 時間: 2025-3-24 05:53 作者: conjunctivitis 時間: 2025-3-24 06:55
Iterative Model Refinement of Recommender MDPs Based on Expert Feedbacks review of the policy. We impose a constraint on the parameters of the model for every case where the expert’s recommendation differs from the recommendation of the policy. We demonstrate that consistency with an expert’s feedback leads to non-convex constraints on the model parameters. We refine t作者: 骯臟 時間: 2025-3-24 11:37 作者: oblique 時間: 2025-3-24 16:05
Continuous Upper Confidence Trees with Polynomial Exploration – Consistencyarch. However, the consistency is only proved in a the case where the action space is finite. We here propose a proof in the case of fully observable Markov Decision Processes with bounded horizon, possibly including infinitely many states, infinite action space and arbitrary stochastic transition k作者: 護航艦 時間: 2025-3-24 19:26
A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimizations field aim to find the optimizer of the function by requesting only a few function evaluations at carefully selected locations. An ideal algorithm should maintain a perfect balance between exploration (probing unexplored areas) and exploitation (focusing on promising areas) within the given evaluat作者: bifurcate 時間: 2025-3-25 02:30 作者: Estimable 時間: 2025-3-25 07:02
Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimizationf resources. We formalize this problem in the framework of bandit optimization as follows: given a set of multiple multi-armed bandits and a budget on the total number of trials allocated among them, select the top-. arms (with high confidence) for as many of the bandits as possible. To solve this p作者: FLIC 時間: 2025-3-25 11:24 作者: HAVOC 時間: 2025-3-25 12:20 作者: impale 時間: 2025-3-25 16:37 作者: Meander 時間: 2025-3-25 20:28 作者: 沉默 時間: 2025-3-26 04:12 作者: 束以馬具 時間: 2025-3-26 07:04 作者: Femine 時間: 2025-3-26 10:57 作者: 保守黨 時間: 2025-3-26 14:27 作者: CURT 時間: 2025-3-26 17:28 作者: 努力趕上 時間: 2025-3-26 21:56 作者: 起來了 時間: 2025-3-27 03:16
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration for this procedure which show the improvement of the order of . for fixed iteration cost over purely sequential versions. Moreover, the multiplicative constants involved have the property of being dimension-free. We also confirm empirically the efficiency of . on real and synthetic problems compared to state-of-the-art competitors.作者: Collision 時間: 2025-3-27 07:54
0302-9743 dings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers 作者: ANTE 時間: 2025-3-27 12:38
Learning from Demonstrations: Is It Worth Estimating a Reward Function?he behavior of the expert. This reward is then optimized to imitate the expert. One can wonder if it is worth estimating such a reward, or if estimating a policy is sufficient. This quite natural question has not really been addressed in the literature right now. We provide partial answers, both from a theoretical and empirical point of view.作者: crease 時間: 2025-3-27 13:56
Regret Bounds for Reinforcement Learning with Policy Adviceis regret and its computational complexity are independent of the size of the state and action space. Our empirical simulations support our theoretical analysis. This suggests RLPA may offer significant advantages in large domains where some prior good policies are provided.作者: 為寵愛 時間: 2025-3-27 20:26
Expectation Maximization for Average Reward Decentralized POMDPsommon set of conditions expectation maximization (EM) for average reward Dec-POMDPs is stuck in a local optimum. We introduce a new average reward EM method; it outperforms a state of the art discounted-reward Dec-POMDP method in experiments.作者: CYN 時間: 2025-3-28 00:51
Iterative Model Refinement of Recommender MDPs Based on Expert Feedbackhe parameters of the model, under these constraints, by partitioning the parameter space and iteratively applying alternating optimization. We demonstrate how the approach can be applied to both flat and factored MDPs and present results based on diagnostic sessions from a manufacturing scenario.作者: heirloom 時間: 2025-3-28 05:38 作者: 古代 時間: 2025-3-28 09:41
Spectral Learning of Sequence Taggers over Continuous Sequences to a class where transitions are linear combinations of elementary transitions and the weights of the linear combination are determined by dynamic features of the continuous input sequence. The resulting learning algorithm is efficient and accurate.作者: 戲服 時間: 2025-3-28 14:22 作者: Insensate 時間: 2025-3-28 16:05 作者: 戰(zhàn)役 時間: 2025-3-28 19:47
978-3-642-40987-5Springer-Verlag Berlin Heidelberg 2013作者: 輕快走過 時間: 2025-3-28 23:07
Machine Learning and Knowledge Discovery in Databases978-3-642-40988-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: muster 時間: 2025-3-29 05:39 作者: 指耕作 時間: 2025-3-29 09:36
Hendrik Blockeel,Kristian Kersting,Filip ?eleznyState-of-the-art research.Up-to-date results.Unique visibility作者: 露天歷史劇 時間: 2025-3-29 11:41
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620509.jpg作者: Cubicle 時間: 2025-3-29 18:21
ie so etwas wie eine Karte des Wissens, der Meinungen und der Einstellungen im Amte zeichnen. Schlie?lich prüften wir, ob sich diese abstrakten Sinnkontexte in den konkreten Meinungen einzelner Dialogpartner wiederfinden lassen. Wir beschreiben also einen Kreis, dessen Ausgangs- und Endpunkt die ein作者: 斷言 時間: 2025-3-29 22:33
Qifeng Qiao,Peter A. Belingder einen Eingabevektor . auf einen Ausgabevektor . abbildet. Fragestellungen dieser Art treten vor allem bei Regelungsproblemen und der Mustererkennung auf. Im folgenden werden Strukturen und Ans?tze zu adaptiven Regelungssystemen in der Robotik vorgestellt.作者: sperse 時間: 2025-3-30 01:52
Bastian Bischoff,Duy Nguyen-Tuong,Torsten Koller,Heiner Markert,Alois Knollrzu ist, da? die inneren Zust?nde des unbekannten Systems beobachtet werden k?nnen. Der zweite Ansatz sieht das unbekannte System als einen stochastischen Automaten, der durch eine Zufallstransitionsmatrix für jeden m?glichen Eingabevektor charakterisiert ist. Verst?rkungstechniken werden von dem Le作者: Facilities 時間: 2025-3-30 07:36 作者: 嫌惡 時間: 2025-3-30 08:19
Meng Fang,Jie Yin,Xingquan Zhu Mehr oder weniger reflektierte Lernwiderst?nde mit defensiven Lerngründen werden insofern vom Autor als subjektiv sinnvolle Handlungsstrategien verstanden. Als lebenslang t?tiger Praktiker erl?utert der Autor, wie die Resultate der Studie für eine gute Praxis betrieblicher Grundbildung genutzt werden k?nnen.978-3-658-39746-3978-3-658-39747-0作者: reception 時間: 2025-3-30 15:32 作者: interpose 時間: 2025-3-30 18:44
Learning Throttle Valve Control Using Policy Search, we employ a policy search RL approach, Pilco [2], to learn a throttle valve control policy. We adapt the Pilco algorithm, while taking into account the practical requirements and constraints for the controller. For evaluation, we employ the resulting algorithm to solve several control tasks in sim作者: Inkling 時間: 2025-3-31 00:40
Model-Selection for Non-parametric Function Approximation in Continuous Control Problems: A Case Stues. However, for efficiency reasons, LWR is used with a limited sample-size, which leads to poor performance without careful tuning of LWR’s parameters. We therefore develop an efficient meta-learning procedure that performs online model-selection and tunes LWR’s parameters based on the Bellman erro作者: 哀求 時間: 2025-3-31 03:45
Knowledge Transfer for Multi-labeler Active Learning labeler to query. Experiments demonstrate that transferring knowledge across related domains can help select the labeler with the best expertise and thus significantly boost the active learning performance.作者: 游行 時間: 2025-3-31 07:31 作者: 6Applepolish 時間: 2025-3-31 09:10 作者: arthroplasty 時間: 2025-3-31 16:20
0302-9743 p discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.978-3-642-40987-5978-3-642-40988-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 胰臟 時間: 2025-3-31 18:29 作者: 慎重 時間: 2025-3-31 23:36
Mohammad Gheshlaghi Azar,Alessandro Lazaric,Emma Brunskill作者: jocular 時間: 2025-4-1 01:56
