Description

This volume introduces a formal representation framework for modelling and reasoning, that allows us to quantify the uncertainty inherent in the use of vague descriptions to convey information between intelligent agents. This can then be applied across a range of applications areas in automated reasoning and learning. The utility of the framework is demonstrated by applying it to problems in data analysis where the aim is to infer effective and informative models expressed as logical rules and relations involving vague concept descriptions. The author also introduces a number of learning algorithms within the framework that can be used for both classification and prediction (regression) problems. It is shown how models of this kind can be fused with qualitative background knowledge such as that provided by domain experts. The proposed algorithms will be compared with existing learning methods on a range of benchmark databases such as those from the UCI repository.

Modelling and Reasoning with Vague Concepts

Product form

£80.99

Includes FREE delivery
RRP: £89.99 You save £9.00 (10%)
Usually despatched within days
Hardback by Jonathan Lawry

1 in stock

Short Description:

This volume introduces a formal representation framework for modelling and reasoning, that allows us to quantify the uncertainty inherent in... Read more

    Publisher: Springer-Verlag New York Inc.
    Publication Date: 11/01/2006
    ISBN13: 9780387290560, 978-0387290560
    ISBN10: 0387290567

    Number of Pages: 246

    Non Fiction , Dictionaries, Reference & Language

    Description

    This volume introduces a formal representation framework for modelling and reasoning, that allows us to quantify the uncertainty inherent in the use of vague descriptions to convey information between intelligent agents. This can then be applied across a range of applications areas in automated reasoning and learning. The utility of the framework is demonstrated by applying it to problems in data analysis where the aim is to infer effective and informative models expressed as logical rules and relations involving vague concept descriptions. The author also introduces a number of learning algorithms within the framework that can be used for both classification and prediction (regression) problems. It is shown how models of this kind can be fused with qualitative background knowledge such as that provided by domain experts. The proposed algorithms will be compared with existing learning methods on a range of benchmark databases such as those from the UCI repository.

    Customer Reviews

    Be the first to write a review
    0%
    (0)
    0%
    (0)
    0%
    (0)
    0%
    (0)
    0%
    (0)

    Recently viewed products

    © 2024 Book Curl,

      • American Express
      • Apple Pay
      • Diners Club
      • Discover
      • Google Pay
      • Maestro
      • Mastercard
      • PayPal
      • Shop Pay
      • Union Pay
      • Visa

      Login

      Forgot your password?

      Don't have an account yet?
      Create account