ridm@nrct.go.th   ระบบคลังข้อมูลงานวิจัยไทย   รายการโปรดที่คุณเลือกไว้

Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering
นักวิจัย : Ali, Shawkat. , Smith, Kate A., 1970-
คำค้น : Pattern perception. , TBA. , 890202 Application Tools and System Utilities. , 080105 Expert Systems. , Expert systems (Computer science) , Kernel functions. , Kernel SVM
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2546
อ้างอิง : http://hdl.cqu.edu.au/10018/44001 , cqu:5660
ที่มา : Ali, S & Smith, K 2003, 'Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering', in Abraham, A, Koppen, M and Franke, K (eds), Design and application of hybrid intelligent systems, IOS Press, Netherlands.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Design and application of hybrid intelligent systems / edited by Ajith Abraham, Mario Ko℗‰ppen and Katrin Franke. The Netherlands : IOS Press Amsterdam, 2003. Chapter 56, p. 553-562 1156 pages 112 chapters 9781586033941 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

Over the last decade kernel based learning algorithms known as Support Vector Machines (SVMs) have become an attractive tool to solve pattern recognition problems. Choosing an appropriate kernel still is a trial and error approach for SVM however. This research provides some insights into the data characteristics that suit particular kernels. Our approach consists of four main stages. First, the performance of six kernels is examined across a collection of 33 classification problems from the machine learning literature. Secondly, a collection of statistics that describe each of the 33 problems in terms of data complexity is collected. After that, fuzzy C-means (FCM) is used to cluster, and construct a decision tree is used to generate the rules of the 33 problems based on these measurea of complexity. Each cluster represents a group of classification problems with similar data characteristics. The performance of each kernel within each cluster and the rules among the tree is then examined in the final stage to provide both quantitative and qualitative insights into which kernels perform best on certain problem types.

บรรณานุกรม :
Ali, Shawkat. , Smith, Kate A., 1970- . (2546). Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Ali, Shawkat. , Smith, Kate A., 1970- . 2546. "Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Ali, Shawkat. , Smith, Kate A., 1970- . "Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2546. Print.
Ali, Shawkat. , Smith, Kate A., 1970- . Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering. กรุงเทพมหานคร : Central Queensland University, Australia; 2546.