Publication Details
Title: Inductive Learning of Compact Rule Sets by Using Effcient Hypotheses Reduction
Author: T. Koch
Group: ICSI Technical Reports
Date: September 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-069.pdf
Overview:
A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called α - reduction. A learning algorithm is described based on α - reduction and analyzed by using probability approximate correct learning results. The results are obtained by reducing a rule set to an equivalent set of kDNF formulas. The goal of the learning algorithm is to induce a compact rule set describing the basic dependencies within a set of data. The reduction is based on criterion which is very flexible and gives a semantic interpretation of the rules which fulfill the criteria. Comparison with syntactical hypotheses reduction show that the α - reduction improves search and has a smaller probability of missclassification.
Bibliographic Information:
ICSI Technical Report TR-92-069
Bibliographic Reference:
T. Koch. Inductive Learning of Compact Rule Sets by Using Effcient Hypotheses Reduction. ICSI Technical Report TR-92-069, September 1992
Author: T. Koch
Group: ICSI Technical Reports
Date: September 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-069.pdf
Overview:
A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called α - reduction. A learning algorithm is described based on α - reduction and analyzed by using probability approximate correct learning results. The results are obtained by reducing a rule set to an equivalent set of kDNF formulas. The goal of the learning algorithm is to induce a compact rule set describing the basic dependencies within a set of data. The reduction is based on criterion which is very flexible and gives a semantic interpretation of the rules which fulfill the criteria. Comparison with syntactical hypotheses reduction show that the α - reduction improves search and has a smaller probability of missclassification.
Bibliographic Information:
ICSI Technical Report TR-92-069
Bibliographic Reference:
T. Koch. Inductive Learning of Compact Rule Sets by Using Effcient Hypotheses Reduction. ICSI Technical Report TR-92-069, September 1992