2011 IEEE 23rd International Conference on Tools with Artificial Intelligence最新文献

Independence-Based MAP for Markov Networks Structure Discovery 基于独立性的马尔可夫网络结构发现MAP

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-12-15 DOI: 10.1109/ICTAI.2011.81

F. Bromberg, F. Schlüter, A. Edera

{"title":"Independence-Based MAP for Markov Networks Structure Discovery","authors":"F. Bromberg, F. Schlüter, A. Edera","doi":"10.1109/ICTAI.2011.81","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.81","url":null,"abstract":"This work presents IBMAP, an approach for robust learning of Markov network structures from data, together with IBMAP-HC, an efficient instantiation of the approach. Existing Score-Based (SB) and Independence-Based (IB) approaches must make concessions either on robustness or efficiency. IBMAP-HC improves robustness efficiently through an IB-SB hybrid approach based on the probabilistic Maximum-A-Posteriori (MAP) technique, and the IB-score, a tractable expression for computing posterior probabilities of Markov network structures. Performance is first tested against IB and SB competitors on synthetic datasets. Against IB competitors (GSMN algorithm and a version of the HHC algorithm adapted here for Markov networks discovery), IBMAP-HC showed reductions in edges Hamming distance with same order running times. Against SB competitors, both IBMAP-HC and our adaptation of HHC produced comparable Hamming distances, but with running times orders of magnitude faster. We also evaluated IBMAP-HC in a realistic, challenging test-bed: EDAs, evolutionary algorithms for optimization that estimate a distribution on each generation. Using IBMAP-HC to estimate distributions, EDAs converged to the optimum faster in all benchmark functions considered, reducing required fitness evaluations by up to 80%.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117117966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Enhancing the Quality of Recommendations through Expert and Trusted Agents 通过专家和可信赖的代理提高推荐的质量

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.56

Fabiana Lorenzi, Mara Abel, S. Loh, André Peres

{"title":"Enhancing the Quality of Recommendations through Expert and Trusted Agents","authors":"Fabiana Lorenzi, Mara Abel, S. Loh, André Peres","doi":"10.1109/ICTAI.2011.56","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.56","url":null,"abstract":"In multi-agent recommender systems, agents are able to generate recommendations according to the preferences of the customer. However, in some domains, specific knowledge is required in order to compose a recommendation and this knowledge may be not available for the agent. In these cases, agents need to communicate with other agents in the community searching for the specific information to complete the recommendation. This paper presents a multi-agent recommender system based on trust and expert agents. It aims at improving the quality of the information exchanged among agents because communication will occur primarily with trusted sources in the hope to decrease the communication load. Also, agents become experts in specific types of recommendation. The approach was validate in the tourism domain by means of recommendations of travel packages and experiments were performed to illustrate the impact of using trust assignment in the quality of the recommendations generated by expert agents. Results corroborate the intuition that expert agents that use a trust mechanism are able to increase the quality of recommendation provided.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115512028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Disassembling and Reconstructing Algorithms for Discrete Event Systems 离散事件系统的分解与重构算法

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.101

Xuena Geng, D. Ouyang, Jinsong Guo, Xiaoyu Wang

引用次数: 0

Ant Colony vs. Genetic Multiobjective Route Planning in Dynamic Multi-hop Ridesharing 动态多跳拼车中蚁群与遗传多目标路线规划

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.50

Wesam Herbawi, M. Weber

引用次数: 25

Towards Optimal Active Learning for Matrix Factorization in Recommender Systems 推荐系统中矩阵分解的最优主动学习

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.182

R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme

{"title":"Towards Optimal Active Learning for Matrix Factorization in Recommender Systems","authors":"R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme","doi":"10.1109/ICTAI.2011.182","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.182","url":null,"abstract":"Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. The optimal active learning selects a query that directly optimizes the expected error for the test data. This approach is applicable for prediction models in which this question can be answered in closed-form given the distribution of test data is known. Unfortunately, there are many tasks and models for which the optimal selection cannot efficiently be found in closed-form. Therefore, most of the active learning methods optimize different, non-optimal criteria, such as uncertainty. Nevertheless, in this paper we exploit the characteristics of matrix factorization, which leads to a closed-form solution and by being inspired from existing optimal active learning for the regression task, develop a method that approximates the optimal solution for recommender systems. Our results demonstrate that the proposed method improves the prediction accuracy of MF.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123683776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Local Discretization of Numerical Data for Galois Lattices 伽罗瓦格数值数据的局部离散化

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.148

Nathalie Girard, K. Bertet, M. Visani

引用次数: 2

A Self Healing Action Composition Agent 一种自愈作用组合剂

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.90

Mohamad El Falou, M. Bouzid, A. Mouaddib, Thierry Vidal

引用次数: 0

Adaptive Batch SOM for Multiple Dissimilarity Data Tables 多不相似数据表的自适应批量SOM

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.92

Anderson Dantas, F. D. Carvalho

引用次数: 8

Toward a Remote-Controlled Weapon-Equipped Camera Surveillance System 遥控武器装备摄像监控系统研究

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.185

Robert Bisewski, P. Atrey

引用次数: 4

Classification by Clusters Analysis - An Ensemble Technique in a Semi-supervised Classification 聚类分析分类——半监督分类中的集成技术

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence Pub Date : 2011-11-07 DOI: 10.1109/ICTAI.2011.137

A. Jurek, Y. Bi, Shengli Wu, C. Nugent

引用次数: 2