Titlebook: Probabilistic Inductive Logic Programming; Luc Raedt,Paolo Frasconi,Stephen Muggleton Book 2008 Springer-Verlag Berlin Heidelberg 2008 Bay

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肥料

978-3-540-78651-1Springer-Verlag Berlin Heidelberg 2008

縮短

Probabilistic Inductive Logic Programming978-3-540-78652-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

fiscal

0302-9743 Overview: 978-3-540-78651-1978-3-540-78652-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

實(shí)施生效

https://doi.org/10.1007/978-3-540-78652-8Bayesian networks; Kernel; algorithmic learning; classifier systems; clustering; computational biology; co

左右連貫

CLP(,): Constraint Logic Programming for Probabilistic Knowledgedatabase or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(.) programs. An implementation of CLP(.) is publicly available as part of YAP Prolog at ..

ascend

Probabilistic Inductive Logic Programmingto the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely ., ., and ., and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.

paleolithic

The Independent Choice Logic and Beyondural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity.

innate

Probabilistic Logic Learning from Haplotype Datatructions from different sources, which can increase accuracy and robustness of reconstruction estimates. Finally, techniques for discovering the structure in haplotype data at the level of haplotypes and population are discussed.

種類

Relational Sequence Learningg and describes several techniques tailored towards realizing this, such as local pattern mining techniques, (hidden) Markov models, conditional random fields, dynamic programming and reinforcement learning.