Fourth International Conference on Hybrid Intelligent Systems (HIS'04)最新文献_第8页

Hybrid soft categorization in conceptual spaces 概念空间中的混合软分类

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.57

Ickjai Lee

引用次数: 4

Robotic hand-eye coordination: from observation to manipulation 机器人手眼协调:从观察到操作

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.82

S. Jafari, R. Jarvis

引用次数: 14

Design of Robust PID Controller With Disturbance Rejection For Motor Using Immune Algorithm 基于免疫算法的电机鲁棒PID控制器设计

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.36

Dong Hwa Kim, Jae-Hoon Cho

引用次数: 4

Reinforcement learning/spl I.bar/hierarchical neuro-fuzzy politree model for control of autonomous agents 强化学习/spl .bar/层次神经模糊政治模型用于自主代理的控制

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.80

Karla Figueiredo, M. Vellasco, M. Pacheco, F. J. Souza

{"title":"Reinforcement learning/spl I.bar/hierarchical neuro-fuzzy politree model for control of autonomous agents","authors":"Karla Figueiredo, M. Vellasco, M. Pacheco, F. J. Souza","doi":"10.1109/ICHIS.2004.80","DOIUrl":"https://doi.org/10.1109/ICHIS.2004.80","url":null,"abstract":"This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence, making it capable, by interacting with its environment, to acquire and retain knowledge for reasoning (infer an action). This new model, named reinforcement learning hierarchical neuro-fuzzy politree (RL-HNFP), descends from the reinforcement learing hierarchical neuro-fuzzy BSP (RL-HNFB) that uses binary space partitioning. By using hierarchical partitioning methods, together with the reinforcement learning (RL) methodology, a new class of neuro-fuzzy systems (SNF) was obtained, which executes, in addition to automatically learning its structure, the autonomous learning of the actions to be taken by an agent. These characteristics represent an important differential when compared with the existing intelligent agents learning systems. The obtained results demonstrate the potential of this new model, which operates without any prior information, such as number of rules, rules specification, or number of partitions that the input space should have.","PeriodicalId":274073,"journal":{"name":"Fourth International Conference on Hybrid Intelligent Systems (HIS'04)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114531266","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: 10

An efficient feature selection using multi-criteria in text categorization 文本分类中基于多准则的高效特征选择

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.20

S. Doan, S. Horiguchi

引用次数: 15

A waterfall model for knowledge management and experience management 知识管理和经验管理的瀑布模型

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.95

Zhaohao Sun

引用次数: 37

Adaptive boosting with leader based learners for classification of large handwritten data 基于leader学习器的大型手写数据分类自适应增强

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.15

T. Babu, M. Murty, V. K. Agrawal

{"title":"Adaptive boosting with leader based learners for classification of large handwritten data","authors":"T. Babu, M. Murty, V. K. Agrawal","doi":"10.1109/ICHIS.2004.15","DOIUrl":"https://doi.org/10.1109/ICHIS.2004.15","url":null,"abstract":"Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for adaptive boosting method, consists of repeated use of a weak or a base learning algorithm to find corresponding weak hypothesis by adapting to the error rates of the individual weak hypotheses. A large, complex handwritten data is under study. A repeated use of weak learner on the huge data results in large amount of processing time. In view of this, instead of using the entire training data for learning, we propose to use only prototypes. Further, in the current work, the base learner consists of a nearest neighbour classifier that employs prototypes generated using \"leader\" clustering algorithm. The leader algorithm is a single pass algorithm and is linear in terms of time as well as computation complexity. The prototype set alone is used as training data. In the process of developing an algorithm, domain knowledge of the Handwritten data, which is under study, is made use of. With the fusion of clustering, prototype selection, AdaBoost and Nearest Neighbour classifier, a very high classification accuracy, which is better than reported earlier on the considered data, is obtained in less number of iterations. The procedure integrates clustering outcome in terms of prototypes with boosting.","PeriodicalId":274073,"journal":{"name":"Fourth International Conference on Hybrid Intelligent Systems (HIS'04)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121460722","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: 5

A hybrid decision support system model for disaster management 灾害管理的混合决策支持系统模型

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.5

S. Asghar, D. Alahakoon, L. Churilov

引用次数: 7

Using associative classification for predicting HIV-1 drug resistance 使用关联分类预测HIV-1耐药性

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.92

Anantaporn Srisawat, B. Kijsirikul

引用次数: 16

Discovering operational signatures with time constraints from a discrete event sequence 从离散事件序列中发现具有时间约束的操作签名

Fourth International Conference on Hybrid Intelligent Systems (HIS'04) Pub Date : 2004-12-05 DOI: 10.1109/ICHIS.2004.37

Philippe Bouché, M. L. Goc

引用次数: 6