標(biāo)題: Titlebook: Agents and Data Mining Interaction; 4th International Wo Longbing Cao,Vladimir Gorodetsky,Philip S. Yu Conference proceedings 2009 Springer [打印本頁] 作者: 誤解 時(shí)間: 2025-3-21 19:49
書目名稱Agents and Data Mining Interaction影響因子(影響力)
書目名稱Agents and Data Mining Interaction影響因子(影響力)學(xué)科排名
書目名稱Agents and Data Mining Interaction網(wǎng)絡(luò)公開度
書目名稱Agents and Data Mining Interaction網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Agents and Data Mining Interaction被引頻次
書目名稱Agents and Data Mining Interaction被引頻次學(xué)科排名
書目名稱Agents and Data Mining Interaction年度引用
書目名稱Agents and Data Mining Interaction年度引用學(xué)科排名
書目名稱Agents and Data Mining Interaction讀者反饋
書目名稱Agents and Data Mining Interaction讀者反饋學(xué)科排名
作者: hemorrhage 時(shí)間: 2025-3-21 23:49 作者: 雀斑 時(shí)間: 2025-3-22 01:20 作者: capsule 時(shí)間: 2025-3-22 05:44
Agent Assignment for Process Management: Pattern Based Agent Performance Evaluationed Pattern based Agent Performance Evaluation (PAPE) and is based on machine learning technique combined with post processing technique. We report on the result of our experiments and discuss issues and improvement of our approach.作者: GLOSS 時(shí)間: 2025-3-22 12:28 作者: 情感脆弱 時(shí)間: 2025-3-22 15:36
Culture Through a Clinical Lensnd false measurement replacement) and generate new control monitoring rules. Various classification algorithms are compared. The performance of the application, as tested via simulation experiments, is discussed.作者: biosphere 時(shí)間: 2025-3-22 17:25 作者: champaign 時(shí)間: 2025-3-23 00:59 作者: ferment 時(shí)間: 2025-3-23 04:51
Culture, Governance and Globalizationapproach integrates traditional mathematical and data mining techniques with a multiagent system. The proposed system is used to build an intrusion detection system (IDS) as a network security application. Finally, experimental results are presented to confirm the good performance of the proposed system.作者: 精確 時(shí)間: 2025-3-23 06:18 作者: 持續(xù) 時(shí)間: 2025-3-23 12:06 作者: 無王時(shí)期, 時(shí)間: 2025-3-23 15:40 作者: 邪惡的你 時(shí)間: 2025-3-23 21:38 作者: PUT 時(shí)間: 2025-3-24 00:16
Equality of Vulnerability and Opportunity components include measures for quantifying private knowledge disclosure, data-mining models suitable for multi-agent predictive data mining, and a set of strategies by which agents can improve their classification accuracy through collaboration. The overall framework and its individual components are validated on a synthetic experimental domain.作者: 一再煩擾 時(shí)間: 2025-3-24 04:12 作者: capillaries 時(shí)間: 2025-3-24 07:51
Agent-Enriched Data Mining Using an Extendable Frameworkcribed and illustrated in terms of two KDD scenarios: meta association rule mining and classifier generation. In conclusion the authors suggest that EMADS provides a sound foundation for both KDD research and application based AEDM.作者: Mediocre 時(shí)間: 2025-3-24 10:54 作者: 冥想后 時(shí)間: 2025-3-24 18:14 作者: Accord 時(shí)間: 2025-3-24 20:04 作者: GUMP 時(shí)間: 2025-3-25 02:29
Ubiquitous Intelligence in Agent Miningrate them, and discuss techniques for involving them into agents, data mining, and agent mining for complex problem-solving. Further investigation on involving and synthesizing ubiquitous intelligence into agents, data mining, and agent mining will lead to a disciplinary upgrade from methodological, technical and practical perspectives.作者: 夜晚 時(shí)間: 2025-3-25 05:08
Jason G. Irizarry,John W. Raiblee distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independ作者: predict 時(shí)間: 2025-3-25 09:17
Jason G. Irizarry,John W. Raibleissues exist in both multi-agent and data mining areas, most of them can be described in terms of or related to ubiquitous intelligence. It is certainly very important to define, specify, represent, analyze and utilize ubiquitous intelligence in agents, data mining, and agent mining. This paper pres作者: 挑剔為人 時(shí)間: 2025-3-25 13:47 作者: 剝皮 時(shí)間: 2025-3-25 17:56 作者: prick-test 時(shí)間: 2025-3-25 20:24
Convivencia: The Goal of Conviviability . (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gat作者: 背景 時(shí)間: 2025-3-26 02:02 作者: CLIFF 時(shí)間: 2025-3-26 06:11 作者: 閑逛 時(shí)間: 2025-3-26 12:31 作者: Countermand 時(shí)間: 2025-3-26 13:46
Culture Through a Clinical Lense combined to enhance the intelligence of the proposed application, mainly in two aspects: increase the reliability of input data (sensor validation and false measurement replacement) and generate new control monitoring rules. Various classification algorithms are compared. The performance of the ap作者: bisphosphonate 時(shí)間: 2025-3-26 17:56 作者: 使成核 時(shí)間: 2025-3-26 23:09 作者: 痛打 時(shí)間: 2025-3-27 01:20
https://doi.org/10.1057/9781137013125ch an ontology is unrealistic and its maintenance is cumbersome. Burden of maintaining a common ontology can be alleviated by enabling agents to evolve their ontologies personally. However, with different ontologies, agents are likely to run into communication problems since their vocabularies are d作者: Frequency-Range 時(shí)間: 2025-3-27 07:03 作者: 搏斗 時(shí)間: 2025-3-27 12:15
Agents and Data Mining Interaction978-3-642-03603-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: committed 時(shí)間: 2025-3-27 15:13
https://doi.org/10.1007/978-3-642-03603-3agent architectures; agent assignment; agent interaction; agent systems implementation; agent technology作者: 熒光 時(shí)間: 2025-3-27 21:11 作者: 生存環(huán)境 時(shí)間: 2025-3-27 22:37 作者: Lasting 時(shí)間: 2025-3-28 02:37
Jason G. Irizarry,John W. RaibleMultiagent systems and data mining techniques are being frequently used in genome projects, especially regarding the annotation process (annotation pipeline). This paper discusses annotation-related problems where agent-based and/or distributed data mining has been successfully employed.作者: 冷峻 時(shí)間: 2025-3-28 08:14 作者: impaction 時(shí)間: 2025-3-28 12:29
Knowledge-Based Reinforcement Learning for Data Mininge distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independ作者: Individual 時(shí)間: 2025-3-28 18:31 作者: custody 時(shí)間: 2025-3-28 21:57
Agents Based Data Mining and Decision Support Systemroduct at the right moment in the product life cycle (PLC), lessens production, storing and other related costs. This arises such problems to be solved as defining the present a PLC phase of a product as also determining a transition point - a moment of time (period), when the PLC phase is changed..作者: Pantry 時(shí)間: 2025-3-29 00:58
Agent-Enriched Data Mining Using an Extendable Frameworkand presents a rational for Agent-Enriched Data Mining (AEDM). A particular challenge of any generic, general purpose, AEDM system is the extensive scope of KDD. To address this challenge the authors suggest that any truly generic AEDM must be readily extendable and propose EMADS, The Extendable Mul作者: affect 時(shí)間: 2025-3-29 04:42
Auto-Clustering Using Particle Swarm Optimization and Bacterial Foraging . (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gat作者: 老人病學(xué) 時(shí)間: 2025-3-29 09:08 作者: 激怒某人 時(shí)間: 2025-3-29 12:53 作者: ENNUI 時(shí)間: 2025-3-29 16:07 作者: ADORN 時(shí)間: 2025-3-29 22:23
Enhancing Agent Intelligence through Data Mining: A Power Plant Case Studye combined to enhance the intelligence of the proposed application, mainly in two aspects: increase the reliability of input data (sensor validation and false measurement replacement) and generate new control monitoring rules. Various classification algorithms are compared. The performance of the ap作者: 秘密會(huì)議 時(shí)間: 2025-3-30 01:59 作者: Adornment 時(shí)間: 2025-3-30 06:50 作者: 幻影 時(shí)間: 2025-3-30 08:51
Concept Learning for Achieving Personalized Ontologies: An Active Learning Approachch an ontology is unrealistic and its maintenance is cumbersome. Burden of maintaining a common ontology can be alleviated by enabling agents to evolve their ontologies personally. However, with different ontologies, agents are likely to run into communication problems since their vocabularies are d作者: 細(xì)胞學(xué) 時(shí)間: 2025-3-30 14:45 作者: insurrection 時(shí)間: 2025-3-30 18:04
