Top‐down induction of first order logical decision trees

By Hendrik Blockeel. 1999

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Citations in the corpus

Fitted Q-Learning for Relational Domains (2020, methodology): “To represent these lifted representations, as mentioned earlier, we use RFGB (Natarajan et al. 2014; Natarajan et al. 2012; Natarajan et al. 2011) and a single tree, TILDE (Blockeel and De Raedt 1998), for approximating the Q-value functions.”
Gradient-based boosting for statistical relational learning: The relational dependency network case (2011, methodology): “Instead of representing the conditional distribution for each attribute (or relation) as a single relational probability tree, we propose to use a set of relational regression trees (Blockeel and De Raedt 1998).”
Non-parametric policy gradients: a unified treatment of propositional and relational domains (2008, methodology): “We apply TreeNPPG to this relational domain by simply employing the relational regression tree learner Tilde ( Blockeel & De Raedt, 1998 ).”
Integrating Guidance into Relational Reinforcement Learning (2004, methodology): “We thus have algorithms for first order logical decision tree induction (Blockeel and De Raedt, 1998), relational distance-based clustering and prediction (Kirsten, Wrobel, and Horvath, 2001), and relational reinforcement learning (Džeroski, De Raedt, and Blockeel, 1998; Džeroski, De Raedt, and…”
First Order Decision Diagrams for Relational MDPs (2007, background): “First order decision trees and even decision diagrams have already been considered in the literature [Blockeel and De Raedt, 1998; Groote and Tveretina, 2003] and several semantics for such diagrams are possible.”
Relational Reinforcement Learning An Overview (2004, methodology): “The TG algorithm (Driessens et al., 2001) is a combination of the Tilde algorithm (Blockeel & De Raedt, 1998) that builds rst order classiication and regression trees and the G-algorithm (Chapman & Kaelbling , 1991) that uses a number of statistical values concerning the performance of each…”
Human-Guided Learning for Probabilistic Logic Models (2018, methodology): “The ψ function is represented using relational regression trees (RRTs)(Blockeel, 1999).”
Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner (2001, background): “A more detailed description of this part of the system can be found in [1].”
Learning higher-order logic programs (2019, methodology): “In this case, Metagolho would unify the atom f([1,2,3],[c,d,e]) with the curry1 metarule.”
Machine learning: a review of classification and combining techniques (2006, background): “Blockeel and De Raedt (1998) studied scaling up first-order logical decision trees with the TILDE 168 S. B. Kotsiantis et al. First order predicate logic rules: • If Father (x,y) and Female(y) then Daughter(x,y) Propositional logic rule with the same meaning: algorithm.”
Seeing the Forest Through the Trees (2007, methodology): “The ACE-hipP system contains a first order decision tree learnerTilde [3], and some ensemble methods such as bagging and first order random forests [19] which use Tilde as the base learner.”
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation (2016, background): “Raedt, 1998 ] , a relational decision tree learner, and perform 5-fold cross validation.”
Statistical Relational Learning for Link Prediction (2003, background): “Tilde [Blockeel and Raedt, 1998] and WARMR [Dehaspe and Toivonen, 1999], for example, extend decision trees and association rules, respectively.”
Multi-Instance Kernels (2002, methodology): “Inductive logic programming algorithms have also been used, for instance, the first-order decision tree learner TILDE (Blockeel & De Raedt, 1998).”