Digitale Bibliotheek
Sluiten Bladeren door artikelen uit een tijdschrift
<< vorige    volgende >>
     Tijdschrift beschrijving
       Alle jaargangen van het bijbehorende tijdschrift
         Alle afleveringen van het bijbehorende jaargang
           Alle artikelen van de bijbehorende aflevering
                                       Details van artikel 2 van 5 gevonden artikelen
  First-Order Logical Neural Networks
Titel: First-Order Logical Neural Networks
Auteur: Kijsirikul, Boonserm
Verschenen in: International journal of hybrid intelligent systems
Paginering: Jaargang 2 (2006) nr. 4 pagina's 253-267
Jaar: 2006-02-09
Inhoud: Inductive Logic Programming (ILP) is a well-known machine learning technique for learning concepts from relational data. Nevertheless, ILP systems are not robust enough to noisy or unseen data in real world domains. Furthermore, in multi-class problems, if the example is not matched with any learned rules, it cannot be classified. This paper presents a novel hybrid learning method to alleviate this restriction by enabling Neural Networks to handle first-order logic programs directly. The proposed method, called First-Order Logical Neural Network (FOLNN), employs the standard feedforward neural network and integrates inductive learning from examples and background knowledge. We also propose a method for determining the appropriate variable substitution in FOLNN learning by using Multiple-Instance Learning (MIL). In the experiments, the proposed method has been evaluated on two first-order learning problems, i.e., the Finite Element Mesh Design and Mutagenesis and compared with the state-of-the-art, the PROGOL system. The experimental results show that the proposed method performs better than PROGOL.
Uitgever: IOS Press
Bronbestand: Elektronische Wetenschappelijke Tijdschriften

                             Details van artikel 2 van 5 gevonden artikelen
<< vorige    volgende >>
 Koninklijke Bibliotheek - Nationale Bibliotheek van Nederland