Select Subject

All (8828)

Agronomy (158)

Animal Husbandry,... (64)

Aquatic Science (16)

Astronomy (36)

Aviation (22)

Basic Medicine (184)

Biology (504)

Chemical Engineering (103)

Chemistry (635)

Civil and Archite... (105)

Clinical Medicine (236)

Computer Science ... (924)

Dynamic and Elect... (121)

Earth Science (119)

Economics (67)

Education (31)

Electrics, Commun... (927)

Energy Science an... (67)

Engineering and T... (15)

Environmental Sci... (140)

Food Science and ... (63)

Forestry (36)

Hydraulic Enginee... (40)

Mechanics (240)

Metallurgical Eng... (32)

Mine Engineering ... (38)

Nuclear Science a... (21)

Pharmacy (98)

Physics (920)

Preventive Medici... (37)

Psychology (20)

Security Science ... (17)

Sports Science (6)

Surveying and Map... (15)

Textile Science a... (16)

Traditional Chine... (56)

Transportation En... (57)

Select/Unselect all

For Selected Papers

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list


	1. INCREMENTAL ON-LINE SKETCHY SHAPE RECOGNITION WITH DYNAMIC MODELING AND RELEVANCE FEEDBACK
	Sun Zheng Xing,Wang Qiang,Yin Jian Feng
	Mechanics 18 May 2004
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In this paper, we have exploited an original principle of the incremental online sketchy shapes recognition. The recognition process is based on two aspects: the dynamic user modeling and relevance feedback. Firstly, we adopt an dynamic user modeling way to build user models for each specific user in an incremental decision tree and recognize dynamically the ‘possible shapes’ by means of fuzzy matching based on the visiting frequency of each recorded shape of user models. Secondly, we make attempt to bring relevance feedback method into the incremental sketchy recognition to to capture users’ input intends and refine the recognition results incrementally. Experiments prove the proposed method both effective and efficient.
	TO cite this article:Sun Zheng Xing,Wang Qiang,Yin Jian Feng. INCREMENTAL ON-LINE SKETCHY SHAPE RECOGNITION WITH DYNAMIC MODELING AND RELEVANCE FEEDBACK[OL].[18 May 2004] http://en.paper.edu.cn/en_releasepaper/content/696


	2. SVM Incremental Learning Algorithm for Adaptive Sketch Recognition
	Sun Zheng Xing,Peng Bin Bin
	Mechanics 17 May 2004
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Adaptation is a critical problem in the design of user-centered human-computer interaction systems. In this paper, an SVM-based incremental learning algorithm is presented to solve this problem for sketch recognition, the goal of which is to achieve adaptive sketch recognition. Our algorithm utilizes only the support vectors instead of all the historical samples, and selects some important samples from all newly added samples as training data. The importance of a sample is measured according to its distance to the hyper-plane of the SVM classifier. Theoretical analysis, experimentation, and evaluation of our algorithm in our on-line graphics recognition system are presented to show the effectiveness of this algorithm. According to our experiments, this algorithm can reduce both the training time and the required storage space for the training dataset to a large extent with very little loss of precision.
	TO cite this article:Sun Zheng Xing,Peng Bin Bin. SVM Incremental Learning Algorithm for Adaptive Sketch Recognition[OL].[17 May 2004] http://en.paper.edu.cn/en_releasepaper/content/692


	3. A Framed Temporal Logic Programming Language
	zhenhuaDuan,Maciej Koutny
	Mechanics 12 May 2004
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:We discuss the projection temporal logic(PTL), based on a primitive projection operator,prj. A framing technique is also presented, using which a synchronization operator,await,is defined within the underlying logic. A framed temporal logic programming language(FTLL) is presented. To illustrate how to use both the language and framing technique,some examples are given.
	TO cite this article:zhenhuaDuan,Maciej Koutny. A Framed Temporal Logic Programming Language[OL].[12 May 2004] http://en.paper.edu.cn/en_releasepaper/content/668


	4. Classify Web Document by Genetic Algorithm with Association Rules
	Tang Changjie,Zhang Tianqing,Hu Rong,Yuan Chang-an,Chen Anlong
	Mechanics 23 March 2004
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Classifying Web Document such as BBS, HTML and e-mail, etc., is an important task for web application. To solve this problem, this paper presents following results: (1) Proposes a new text classification method called Classification by Genetic Algorithm with Association Rules Method (CGAA method). (2) Other than previous work, the fitness function are applied under the guidance of the association rules mined by Apriori_CGAA algorithm. (3) Realizing a family of genetic procedures such as CGAA _Roulette_Selection, CGAA_Xover and CGAA _binaryMutation and giving extensive experiments with real data. (4)The experiment show that the CGAA algorithm is superior to other common methods. A Best-Vector with a score 3513.6 can be achieved after running CGAA algorithm after 50 generations.
	TO cite this article:Tang Changjie,Zhang Tianqing,Hu Rong, et al. Classify Web Document by Genetic Algorithm with Association Rules[OL].[23 March 2004] http://en.paper.edu.cn/en_releasepaper/content/522

Select/Unselect all

For Selected Papers

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list

Rank

Alert Name:
Alerting to:
Authentication email will be sent to your email address in 24 hours
Frequency:
Email Message Format:	Plain text Graphical(HTML)

Complete the form below and we will recommend the selected titles to your friends on your behalf. * Indicates a required field.
Your name*:
Your email address*:
Recipient's name*:
Recipient's email address*:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find the page(s) useful.

Your name:
Your email address:
Recipient's name:
Recipient's email address:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find this page useful.

Disclaimer: This message was sent to your friend using the "Send it to a friend" facility on the Sciencepaper Online’ WWW site, http://www.paper.edu.cn/en. The Sciencepaper Online is not responsible for the content of this email, and anything said in this email does not necessarily reflect the Sciencepaper Online's views.

	Check out RSS, or use RSS reader to subscribe this item