Isbn: 9780792393740 - multistrategy learning: a special issue of machine learning (the springer international series in engineering and computer science, band 240) (3 Ergebnisse)

ISBN: 
Mit der Detailsuche verfeinern

Optimieren Sie Ihre Suche

  • Bücher (3)

  • Neu (3)

bis

Benutzerdefinierte Preisspanne (EUR)

bis

  • Sprache: Englisch

    Verlag: Springer, 1993

    0792393740 / 9780792393740

    • Hardcover

    Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections

    Verkäufer/-in mit 5 Sternen
    Verkäufer/-in kontaktieren

    Zustand: Neu

    EUR 252,86

    EUR 13,31 Versand 
    Versand von Vereinigtes Königreich nach USA

    Anzahl: Mehr als 20 verfügbar

    Zustand: New. In English.

  • Sprache: Englisch

    Verlag: Springer US, 1993

    0792393740 / 9780792393740

    • Hardcover

    Anbieter: moluna, Greven, Deutschlandmoluna

    Verkäufer/-in mit 5 Sternen
    Verkäufer/-in kontaktieren

    Zustand: Neu

    EUR 246,91

    EUR 48,99 Versand 
    Versand von Deutschland nach USA

    Anzahl: Mehr als 20 verfügbar

    Gebunden. Zustand: New. Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for.

  • Sprache: Englisch

    Verlag: Springer Nature B.V. Jun 1993, 1993

    0792393740 / 9780792393740

    • Hardcover

    Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

    Verkäufer/-in mit 5 Sternen
    Verkäufer/-in kontaktieren

    Zustand: Neu

    EUR 310,49

    EUR 35,00 Versand 
    Versand von Deutschland nach USA

    Anzahl: 2 verfügbar

    Buch. Zustand: Neu. Neuware - Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.…