Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Articles: DFG German National Licenses  (3)
  • noise  (2)
  • 20B25  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Automorphisms and isotone self-maps of ordered sets with top and bottom (1993)
    Springer
    Order 10 (1993), S. 105-110 
    Details
    ISSN: 1572-9273
    Keywords: 06A07 ; 06A12 ; 05E25 ; 20B25 ; Automorphism ; isotone self-map ; irreducible element
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract For an ordered setP letP P denote the set of all isotone self-maps on P, that is, all mapsf fromP toP such thatx≥y impliesf(x)≥f(y), and let Aut (P) the set of all automorphisms onP, that is, all bijective isotone self-maps inP P . We establish an inequality relating ¦P P ¦ and ¦Aut(P)¦ in terms of the irreducibles ofP. As a straightforward corollary, we show that Rival and Rutkowski's automorphism conjecture is true for lattices. It is also true for ordered sets with top and bottom whose covering graphs are planar.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01111294
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Bias in information-based measures in decision tree induction (1994)
    Springer
    Machine learning 15 (1994), S. 321-329 
    Details
    ISSN: 0885-6125
    Keywords: Decision trees ; noise ; induction ; unbiased attribute selection ; information-based measures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A fresh look is taken at the problem of bias in information-based attribute selection measures, used in the induction of decision trees. The approach uses statistical simulation techniques to demonstrate that the usual measures such as information gain, gain ratio, and a new measure recently proposed by Lopez de Mantaras (1991) are all biased in favour of attributes with large numbers of values. It is concluded that approaches which utilise the chi-square distribution are preferable because they compensate automatically for differences between attributes in the number of levels they take.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993349
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Technical Note: Bias in Information-Based Measures in Decision Tree Induction (1994)
    Springer
    Machine learning 15 (1994), S. 321-329 
    Details
    ISSN: 0885-6125
    Keywords: Decision trees ; noise ; induction ; unbiased attribute selection ; information-based measures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A fresh look is taken at the problem of bias in information-based attribute selection measures, used in the induction of decision trees. The approach uses statistical simulation techniques to demonstrate that the usual measures such as information gain, gain ratio, and a new measure recently proposed by Lopez de Mantaras (1991) are all biased in favour of attributes with large numbers of values. It is concluded that approaches which utilise the chi-square distribution are preferable because they compensate automatically for differences between attributes in the number of levels they take.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022694010754
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...