http://biblioteca.universia.net/vernivel.do?nivel=1104 Mostrando recursos 1 - 20 de 2,125 es http://biblioteca.universia.net/img/logotipo.jpg http://biblioteca.universia.net/ http://biblioteca.universia.net/ficha.do?id=46515676 We apply methods of abduction derived from propositional probabilistic reasoning to predicate probabilistic reasoning, in particular inductive logic, by treating finite predicate knowledge bases as potentially infinite propositional knowledge bases. It is shown that for a range of predicate knowledge bases (such as those typically associated with inductive reasoning) and several key propositional inference processes (in particular the Maximum Entropy Inference Process) this procedure is well... Inductive Logic Paris http://biblioteca.universia.net/ficha.do?id=41648471 ) Johannes Furnkranz E-mail: juffi@ai.univie.ac.at Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna, Austria Inductive Logic Programming Inductive Logic Programming (ILP) can be viewed as research in the intersection of Logic Programming and inductive Machine Learning. Informally speaking the field is concerned with the induction of PROLOG programs. Being able to express the discovered knowledge in a first-order logic representation language can overcome ... A Short http://biblioteca.universia.net/ficha.do?id=41650551 ) Johannes Furnkranz E-mail: juffi@ai.univie.ac.at Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna, Austria Inductive Logic Programming Inductive Logic Programming (ILP) can be viewed as research in the intersection of Logic Programming and inductive Machine Learning. Informally speaking the field is concerned with the induction of PROLOG programs. Being able to express the discovered knowledge in a first-order logic representation language can overcome ... A Short http://biblioteca.universia.net/ficha.do?id=41813325 ) Johannes Furnkranz E-mail: juffi@ai.univie.ac.at Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Vienna, Austria Inductive Logic Programming Inductive Logic Programming (ILP) can be viewed as research in the intersection of Logic Programming and inductive Machine Learning. Informally speaking the field is concerned with the induction of PROLOG programs. Being able to express the discovered knowledge in a first-order logic representation language can overcome ... A Short http://biblioteca.universia.net/ficha.do?id=46889907 The generic task of Inductive Logic Programming (ILP) is to search a predefined subspace of first-order logic for hypotheses that in some respect explain examples and background knowledge. In this paper we consider the development of parallel implementations of ILP systems. A first part discusses the division of the ILP-task into subtasks that can be handled concurrently by multiple processes executing a common sequential ILP algorithm. We define the notion of a valid partition of an ILP-task... Luc Dehaspe; Luc De Raedt http://biblioteca.universia.net/ficha.do?id=46894859 The generic task of Inductive Logic Programming (ILP) is to search a predefined subspace of first-order logic for hypotheses that in some respect explain examples and background knowledge. In this paper we consider the development of parallel implementations of ILP systems. A first part discusses the division of the ILP-task into subtasks that can be handled concurrently by multiple processes executing a common sequential ILP algorithm. We define the notion of a valid partition of an ILP-task... Luc Dehaspe; Luc De Raedt http://biblioteca.universia.net/ficha.do?id=46891105 The generic task of Inductive Logic Programming (ILP) is to search a predefined subspace of first-order logic for hypotheses that in some respect explain examples and background knowledge. In this paper we consider the development of parallel implementations of ILP systems. A first part discusses the division of the ILP-task into subtasks that can be handled concurrently by multiple processes executing a common sequential ILP algorithm. We define the notion of a valid partition of an ILP-task... Luc Dehaspe; Luc De Raedt http://biblioteca.universia.net/ficha.do?id=41486015 Inductive Logic Programming (ILP) can be viewed as research in the intersection of Logic Programming and inductive Machine Learning. Informally speaking the field is concerned with the induction of PROLOG programs. Being able to express the discovered knowledge in a first-order logic representation language can overcome some of the limitations of classical learning algorithms. The representational power of these algorithms is usually restricted to propositional domain theories such as decisio... A Short http://biblioteca.universia.net/ficha.do?id=42088074 Nonmonotonic logic programming (NMLP) and inductive logic programming (ILP) are two important extensions of logic programming. Chiaki Sakama http://biblioteca.universia.net/ficha.do?id=41472441 Inductive Logic Programming (ILP) is closely related to Logic Programming (LP) by the name. We extract the basic differences of ILP and LP by comparing both and give definitions of the basic assumptions of their paradigms, e.g. closed world assumption, the open domain assumption and the open world assumption used in ILP. We then define a three--valued semantic of ILP and point out relations between our semantic and the framework of Plotkin, [Plotkin, 1971], and of Helft, [Helft, 1989]. Finall... Siegfried Bell,Steffo Weber http://biblioteca.universia.net/ficha.do?id=41634809 Inductive Logic Programming (ILP) is closely related to Logic Programming (LP) by the name. We extract the basic differences of ILP and LP by comparing both and give definitions of the basic assumptions of their paradigms, e.g. closed world assumption, the open domain assumption and the open world assumption used in ILP. We then define a three--valued semantic of ILP and point out relations between our semantic and the framework of Plotkin, [Plotkin, 1971], and of Helft, [Helft, 1989]. Finall... Siegfried Bell,Steffo Weber http://biblioteca.universia.net/ficha.do?id=41652455 . This paper is concerned with learning from positive and negative examples expressed in first-order logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discriminant induction problem is shown to be equivalent to a Constraint Satisfaction Problem (CSP): all constrained clauses covering positive examples and rejecting negative examples can be trivially deri... http://biblioteca.universia.net/ficha.do?id=41510074 . This paper is concerned with learning from positive and negative examples expressed in first-order logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discriminant induction problem is shown to be equivalent to a Constraint Satisfaction Problem (CSP): all constrained clauses covering positive examples and rejecting negative examples can be trivially deri... http://biblioteca.universia.net/ficha.do?id=41796942 . This paper is concerned with learning from positive and negative examples expressed in first-order logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discriminant induction problem is shown to be equivalent to a Constraint Satisfaction Problem (CSP): all constrained clauses covering positive examples and rejecting negative examples can be trivially deri... http://biblioteca.universia.net/ficha.do?id=46702123 Inductive Logic Programming (ILP) is often situated as a research area emerging at the intersection of Machine Learning and Logic Programming (LP). This paper makes the link more clear between ILP and LP, in particular, between ILP and Abductive Logic Programming (ALP), i.e., LP extended with abductive reasoning. We formulate a generic framework for handling incomplete knowledge. This framework can be instantiated both to ALP and ILP approaches. By doing so more light is shed on the relations... Hilde Ade; Marc Denecker http://biblioteca.universia.net/ficha.do?id=47824245 We apply methods of abduction derived from propositional probabilistic reasoning to predicate probabilistic reasoning, in particular inductive logic, by treating nite predicate knowledge bases as potentially in nite propositional knowledge bases. It is shown that for a range of predicate knowledge bases (such as those typically associated with inductive reasoning) and several key propositional inference processes (in particular the Maximum Entropy Inference Process) this procedure is well ... J. B. Paris; M. Wafy http://biblioteca.universia.net/ficha.do?id=46333001 This paper suggests an experimental approach of how to apply inductive logic programming in the discovery of web usage patterns in the form of first-order rules representing user sessions. Such rules may be used to improve the quality and the performance of a web site. The experiment has been done using the Progol Inductive Logic Programming System, and the data source are log files from an Apache web server. Amund Tveit http://biblioteca.universia.net/ficha.do?id=41833929 . Given a target theory to learn that will continuously change over time, an Inductive Logic Programming (ILP) algorithm would have to constantly revise its current hypothesis. Instance-based learning methods step round the need for theory revision, but don't allow the same modelling of the underlying theory that we can accomplish using standard ILP. In addition, the representations of the concepts modelled by instance based-learning are not easily understood. We present a method for learning... Heather Maclaren http://biblioteca.universia.net/ficha.do?id=46430311 When comparing inductive logic programming (ILP) and attributevalue learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current inductive logic programming systems are small according to general standards within the data mining community. The main source of inefficiency lies in the assumption that several examples may be relate... Hendrik Blockeel; Hendrik Blockeel; Luc De Raedt; Luc De Raedt; Nico Jacobs; Nico Jacobs; Bart Demoen; Bart Demoen http://biblioteca.universia.net/ficha.do?id=46436790 Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning and computational logic. The paper gives an introduction to this area based on a distinction between two different semantics used in inductive logic programming, and illustrates their application in knowledge discovery and programming. Whereas most research in inductive logic programming has focussed on learning single predicates from given datasets using the normal ILP semantics (e.g. the w... Luc De Raedt; Nada Lavrac