L. De Raedt, H. Blockeel. Using logical decision trees for clustering. Proc. Seventh Int. Workshop on Inductive Logic Programming, Springer, LNAI 1297, pp. 133--140, 1997. Home/Search Document Details and Download
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Distance Measures Between Atoms - Ramon, Van Laer, Bruynooghe (1998) (Correct)
....years there is a growing interest in first order learners, however existing proposals for distances between non ground atoms have some drawbacks. In this paper we develop a new measure for the distance between nonground atoms. 1 Introduction In learning systems based on clustering (e.g. C0.5 [3], KBG [1] and in instance based learning (e.g. 9, ch.4] RIBL [6] a measure of the distance between objects is an essential component. Good measures exist for distances between objects in an attribute value representation (see e.g. 9, ch. 4] Recently there is a growing interest in using ....
....and t 1 ; t n terms, f(t 1 ; t n ) is a term. An atom is of the form p(t 1 ; t n ) with p=n a predicate and t 1 ; t n terms. We denote the set of all atoms with A. We will use the notion of position as defined in [7] Positions are sequences of positive integers (e.g. [2,3,2]) elements of N . We use the symbols u; v; w; to denote positions. denotes the empty position, and Delta the concatenation operation on positions. The relation v in N defined by u v v , 9w; v = u Delta w is the prefix order. With t a term or atom, the set of positions of t, ....
L. De Raedt and H. Blockeel. Using logical decision trees for clustering. In Proceedings of the 7th International Workshop on Inductive Logic Programming, volume 1297 of Lecture Notes in Artificial Intelligence, pages 133--141. SpringerVerlag, 1997.
Computational Logic and Machine Learning: A roadmap for Inductive .. - Lavrac (1999) (1 citation) (Correct)
.... By relaxing the strict notion of explanation used in clausal discovery [13] to allow for theories that satisfy some other acceptance criteria (similarity, associativity, interestingness) descriptive ILP can be extended to incorporate learning of association rules [8] first order clustering [12, 21, 36], database restructuring [24, 54] subgroup discovery [58] learning qualitative models [31] and equation discovery [19] 2.2.3 An example illustrating predictive and descriptive ILP Consider a problem of learning family relations where the predictive knowledge discovery task is to define the ....
....learning of association rules from multiple relations. First order clustering. Top down induction of decision trees can be viewed as a clustering method since nodes in the tree correspond to sets of examples with similar properties, thus forming concept hierarchies. This view was adopted in C0.5 [12], an upgrade of the Tilde logical decision tree learner. A relational distance based clustering is presented also in [36] An early approach combining learning and conceptual clustering techniques was implemented in the system Cola [21] Given a small (sparse) set of classified training instances ....
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L. De Raedt, H. Blockeel. Using logical decision trees for clustering. Proc. Seventh Int. Workshop on Inductive Logic Programming, Springer, LNAI 1297, pp. 133--140, 1997.
Relational Reinforcement Learning - Dzeroski, De Raedt, Blockeel (1998) (23 citations) Self-citation (De raedt Blockeel) (Correct)
....task involves learning a policy to select actions. Learning is necessary as the planning agent does not know the effects of its actions. Relational reinforcement learning employs the Q learning method [14, 8, 11] where the Q function is learned using a relational regression tree algorithm (see [6, 9]) A state is represented relationally as a set of ground facts. A relational regression tree in this context takes as input a relational description of a state, a goal and an action, and produces the corresponding Q value. This paper is organized as follows. In section 2, we view planning (under ....
....3.2 is in the for loop where the Q function is modified. This for loop now becomes : for j=i 1 to 0 do generate example (s j ; a j ; q j ) where q j : r i flmax a 0 Q e (s j 1 ; a 0 ) update Q e using TILDE RT to produce Q e 1 using the examples (s j ; a j ; q j ) TILDE RT [6] is an algorithm for learning logical regression trees and will be described briefly below. The initial tree Q 0 assigns zero value to all state action pairs. From each goal state g encountered, an example (g,a,0) is generated for each action a whose preconditions are satisfied in g. The ....
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De Raedt, L., and Blockeel, H. (1997) Using logical decision trees for clustering. In Proc. 7th Intl. Workshop on Inductive Logic Programming, pages 133--141, Springer, Berlin.
Relational Data Mining and Subgroup Discovery - Lavrac (2002) (Correct)
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L. De Raedt, H. Blockeel. Using logical decision trees for clustering. Proc. Seventh Int. Workshop on Inductive Logic Programming, Springer, LNAI 1297, pp. 133--140, 1997.
Learning of Class Descriptions from Class.. - Geibel, Schädler.. (2002) (Correct)
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L. De Raedt and H. Blockeel. Using logical decision trees for clustering. In N. Lavrac and S. Dzeroski, editors, Proc. of the 7th Int. WS on ILP, volume 1297 of LNAI, pages 133-140. Springer, September17-20 1997.
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