The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 53-719
Full Name: Ali Al-ibrahim
Position: Assistant Professor
Age: ON
Sex: Male
Address: P.O. Box 1101, Amman 11947, Jordan
Country: JORDAN
Tel: 00962799112999
Tel prefix: 00962777060748
Fax: 0096265063042
E-mail address: alikitim@yahoo.com
Other E-mails: ali.alibrahim@wise.edu.jo
Title of the Paper: Continuous Inductive Learning Algorithm(CILA)
Authors as they appear in the Paper: Ali Al-Ibrahim
Email addresses of all the authors: ali.alibrahim@wise.edu.jo,alikitim@yahoo.com
Number of paper pages: 10
Abstract: Abstract: -Most of the existing machines learning algorithms are able to extract knowledge from databases that store discrete attributes (features). If the attributes are continuous, the algorithms can be integrated with a discretization algorithm that transforms them into discrete attributes. Inductive learning system can be effectively used to acquire classification knowledge from examples; many existing symbolic learning algorithms can be applied in domains with continuous attributes when integrated with discretization algorithms to transform the continuous attributes into ordered discretization. In this paper we discuss machine learning, especially inductive learning and its related algorithms, and anew information theoretic discretization method optimized for supervised and unsupervised learning methods proposed and described, so we proposed improvement of ILA [7] which called Continuous Inductive Learning Algorithm (CILA), and tested in inductive learning ex!
ample to show how the discretization methods deal with continuous attributes.
Keywords: Continuous Inductive Learning, discretization methods, and inductive learning
EXTENSION of the file: .pdf
Special (Invited) Session:
Organizer of the Session:
How Did you learn about congress: machine learning
IP ADDRESS: 77.245.3.42
