作者机构：
[欧阳竞成; 林亚平; 张建明] College of Computer and Communication, Hu'nan University, Changsha 410082, China;[林亚平; 周四望] College of Software, Hu'nan University, Changsha 410082, China;[张建明] Department of Computer Science, Hu'nan City University, Yiyang 413000, China
通讯机构：
College of Computer and Communication, Hu'nan University, China
期刊：
The Journal of Information and Computational Science,2010年7(13):2739-2748 ISSN：1548-7741
通讯作者：
Jin, H.(jinhuixia1980@163.com)
作者机构：
[Jin, Huixia] Department of Physics and Telecommunication Engineering, Hunan City University, Yiyang, 413000, China;[Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang, 413000, China
通讯机构：
Department of Physics and Telecommunication Engineering, Hunan City University, China
关键词：
Clonal selection algorithm;Genetic algorithm;Information retrieval;Parallel genetic immune clonal algorithm;Vector space model
作者机构：
Department of Computer Science,Hunan City University,Yiyang 413000,China;School of Computer,University of Electronic Science & Technology of China,Chengdu 610054,China
会议名称：
2010 International Forum on Computer Science-Technology and Applications(2010 国际计算机科学技术应用论坛 IFCSTA 2010)
会议时间：
2010-12-10
会议地点：
南宁
会议论文集名称：
2010 International Forum on Computer Science-Technology and Applications(2010 国际计算机科学技术应用论坛 IFCSTA 2010)论文集
关键词：
Turing machinejuring computable;compute model
摘要：
Turing-computable issue Is important in research of Turing Machine and has significant value in both theory and practice. The paper analyzes Turingcomputable issue of k-variable function of nonnegative numbers by relational operationsfincludes greater than,less than and equal) and arithmetic operationsfincludes add operation,subtract,multiply,divide and modulo) and different accepting states are designed to compare the non-negative integers and to distinguish normal subtraction and proper subtraction and to decide whether an integer could be divided exactly. The paper also propose a Turing computation model for non-negative integers and based on the model,the conversion between binary numbers and non-negative integers in decimal system is achieved.
期刊：
Journal of Convergence Information Technology,2010年5(9):118-125 ISSN：1975-9320
通讯作者：
Wang, F.(wangfengxia@gmail.com)
作者机构：
[Chang, Xiao; Wang, Fengxia] Department of Computer Science and Technology, Xi'an Jiaotong University, China;[Jin, Huixia] Department of Physics and Telecom Engineering, Hunan City University, Yiyang, 413008, China
摘要：
In recent years, learning ranking function for information retrieval has drawn the attentions of the researchers from information retrieval and machine learning community. In existing approaches of learning to rank, the sparse prediction model only can be learned by support vector learning approach. However, the number of support vectors grows steeply with the size of the training data set. In this paper, we propose a sparse Bayesian kernel approach to learn ranking function. By this approach accurate prediction models can be derived, which typically utilize fewer basis functions than the comparable SVM-based approaches while offering a number of additional advantages. Experimental results on document retrieval data set show that the generalization performance of this approach competitive with two state-of-the-art approaches and the prediction model learned by it is typically sparse.
作者机构：
[黄龙杨; 刘慧; 程恩] Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Xiamen 361005, Fujian, China;[李梦醒] Department of Physics and Telecommunications, Hunan City University, Yiyang 413000, Hunan, China
通讯机构：
Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, China
摘要：
It is well known that information retrieval systems based entirely on syntactic contents have serious limitations. In order to achieve high precision and recall on IR systems, the incorporation of natural language processing techniques that provide semantic information is needed. For this reason, by determining the semantic for the constituents of documents, a clustering method is presented in this paper. The goal is to find the conjoined point which can combine the advantages of both textual part and visual part, and to use for IR systems. It can help to well extract the meaning of a term. Thus, we can take the formalized meaning, instead of the lexical term, and consequently resolve the word sense ambiguity. Experimental results show that the proposed SWCSM model significantly improves the average precision and recall and reduces the overall search time.
