IDSIA

Displaying 1-3 of 3 results

  • White Papers // Oct 2010

    Principles and Applications of Swarm Intelligence for Adaptive Routing in Telecommunications Networks

    In the past few years there has been a lot of research on the application of swarm intelligence to the problem of adaptive routing in telecommunications networks. A large number of algorithms have been proposed for different types of networks, including wired networks and wireless ad hoc networks. In this...

    Provided By IDSIA

  • White Papers // Mar 2009

    Evolving Memory Cell Structures for Sequence Learning

    The problem of sequence learning is to learn the underlying function of a dynamic system, so as to be able either to produce the next step in a sequence produced by the system (sequence prediction), or correctly classify a sequence produced by the system (sequence classification). The best recent supervised...

    Provided By IDSIA

  • White Papers // Dec 2008

    JNCC2: The Java Implementation of Naive Credal Classifier 2

    JNCC2 implements the Naive Credal Classifier 2 (NCC2). This is an extension of naive Bayes to imprecise probabilities that aims at delivering robust classifications also when dealing with small or incomplete data sets. Robustness is achieved by delivering set-valued classifications (that is, returning multiple classes) on the instances for which...

    Provided By IDSIA

  • White Papers // Oct 2010

    Principles and Applications of Swarm Intelligence for Adaptive Routing in Telecommunications Networks

    In the past few years there has been a lot of research on the application of swarm intelligence to the problem of adaptive routing in telecommunications networks. A large number of algorithms have been proposed for different types of networks, including wired networks and wireless ad hoc networks. In this...

    Provided By IDSIA

  • White Papers // Dec 2008

    JNCC2: The Java Implementation of Naive Credal Classifier 2

    JNCC2 implements the Naive Credal Classifier 2 (NCC2). This is an extension of naive Bayes to imprecise probabilities that aims at delivering robust classifications also when dealing with small or incomplete data sets. Robustness is achieved by delivering set-valued classifications (that is, returning multiple classes) on the instances for which...

    Provided By IDSIA

  • White Papers // Mar 2009

    Evolving Memory Cell Structures for Sequence Learning

    The problem of sequence learning is to learn the underlying function of a dynamic system, so as to be able either to produce the next step in a sequence produced by the system (sequence prediction), or correctly classify a sequence produced by the system (sequence classification). The best recent supervised...

    Provided By IDSIA