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Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
| Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 215 p. 56 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Data mining Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Data Mining and Knowledge Discovery |
| ISBN | 3-319-40566-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter Learning for Probabilistic Ontologies -- 1 Introduction -- 2 Description Logics -- 3 Semantics and Reasoning in Probabilistic DLs -- 4 Parameter Learning for Probabilistic DLs -- 5 Distributed Parameter Learning for Probabilistic DLs -- 5.1 Architecture -- 5.2 MapReduce View -- 5.3 Scheduling Techniques -- 5.4 Overall EDGEMR -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Meta-Interpretive Learning of Data Transformation Programs -- 1 Introduction -- 2 Related Work -- 3 Framework -- 4 Implementation -- 4.1 Transformation Language -- 5 Experiments -- 5.1 XML Data Transformations -- 5.2 Ecological Scholarly Papers -- 5.3 Patient Medical Records -- 6 Conclusion and Further Work -- A Appendix 1 -- B Appendix 2 -- References -- Statistical Relational Learning with Soft Quantifiers -- 1 Introduction -- 2 PSLQ: PSL with Soft Quantifiers -- 3 Inference and Weight Learning in PSLQ -- 3.1 Inference -- 3.2 Weight Learning -- 4 Evaluation: Trust Link Prediction -- 5 Conclusion -- References.
Ontology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method. 4.3 Results for Warmr/TreeLiker Combined Method -- 5 Conclusions -- References -- kProbLog: An Algebraic Prolog for Kernel Programming -- 1 Introduction -- 2 KProbLogS -- 3 kProbLog -- 3.1 Recursive kProbLog Program with Meta-Functions -- 3.2 The Jacobi Method -- 3.3 kProbLog TP-Operator with Meta-Functions -- 4 kProbLogS[x] -- 4.1 Polynomials for Feature Extraction -- 4.2 The @id Meta-Function -- 5 Graph Kernels -- 5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels -- 5.2 Graph Invariant Kernels -- 6 Conclusions -- References -- An Exercise in Declarative Modeling for Relational Query Mining -- 1 Introduction -- 2 Problem Statement -- 3 Encoding -- 4 First Order Model -- 5 Experiments -- 6 Model Discussion and Generalization -- 7 Related Work -- 8 Conclusions -- A Appendix: Introduction to IDP -- References -- Learning Inference by Induction -- 1 Introduction -- 2 Learning Logical Inference -- 2.1 Learning Logics -- 2.2 Learning from 1-Step Transitions -- 2.3 Learning Deduction Rules by LF1T -- 3 Learning Non-logical Inference Rules -- 3.1 Abduction -- 3.2 Frame Axiom -- 3.3 Conversational Implicature -- 4 Discussion -- 5 Conclusion -- References -- Identification of Transition Models of Biological Systems in the Presence of Transition Noise -- 1 Introduction -- 2 Transition Identification Under Transition Noise -- 3 Empirical Evaluation -- 3.1 Problems -- 3.2 Data -- 3.3 Models -- 3.4 Algorithms and Machines -- 3.5 Method -- 3.6 Results -- 3.7 Transition Identification Worked Example: Water -- 4 Related Work -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910482960503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
| Inductive Logic Programming [[electronic resource] ] : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers / / edited by Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 215 p. 56 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Data mining Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Data Mining and Knowledge Discovery |
| ISBN | 3-319-40566-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Relational Kernel-Based Grasping with Numerical Features -- 1 Introduction -- 2 The Robot Grasping Scenario and Grasping Primitives -- 3 Relational Grasping: Problem Formulation -- 3.1 Data Modeling -- 3.2 Declarative and Relational Feature Construction -- 3.3 The Relational Problem Definition -- 3.4 Graphicalization -- 4 Relational Kernel Features -- 5 Experiments -- 5.1 Dataset and Evaluation -- 5.2 Results and Discussion -- 6 Related Work -- 7 Conclusions -- References -- CARAF: Complex Aggregates within Random Forests -- 1 Introduction and Context -- 2 Complex Aggregates -- 3 Random Forests -- 4 CARAF: Complex Aggregates with RAndom Forests -- 5 Experimental Results -- 6 Aggregation Processes Selection with Random Forests -- 7 Conclusion and Future Work -- References -- Distributed Parameter Learning for Probabilistic Ontologies -- 1 Introduction -- 2 Description Logics -- 3 Semantics and Reasoning in Probabilistic DLs -- 4 Parameter Learning for Probabilistic DLs -- 5 Distributed Parameter Learning for Probabilistic DLs -- 5.1 Architecture -- 5.2 MapReduce View -- 5.3 Scheduling Techniques -- 5.4 Overall EDGEMR -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Meta-Interpretive Learning of Data Transformation Programs -- 1 Introduction -- 2 Related Work -- 3 Framework -- 4 Implementation -- 4.1 Transformation Language -- 5 Experiments -- 5.1 XML Data Transformations -- 5.2 Ecological Scholarly Papers -- 5.3 Patient Medical Records -- 6 Conclusion and Further Work -- A Appendix 1 -- B Appendix 2 -- References -- Statistical Relational Learning with Soft Quantifiers -- 1 Introduction -- 2 PSLQ: PSL with Soft Quantifiers -- 3 Inference and Weight Learning in PSLQ -- 3.1 Inference -- 3.2 Weight Learning -- 4 Evaluation: Trust Link Prediction -- 5 Conclusion -- References.
Ontology Learning from Interpretations in Lightweight Description Logics -- 1 Introduction -- 2 Description Logic Preliminaries -- 3 Learning Model -- 4 Finite Learning Sets -- 5 Learning Algorithms -- 6 Related Work -- 7 Conclusions and Outlook -- References -- Constructing Markov Logic Networks from First-Order Default Rules -- 1 Introduction -- 2 Background -- 2.1 Markov Logic Networks -- 2.2 Reasoning About Default Rules in System P -- 3 Encoding Ground Default Theories in Markov Logic -- 4 Encoding Non-ground Default Theories in Markov Logic -- 5 Evaluation -- 6 Conclusion -- A Proofs -- References -- Mine 'Em All: A Note on Mining All Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Graph Mining Problems -- 4 Mining All (Induced) Subgraphs -- 4.1 Negative Results -- 4.2 Positive Results for ALLF I and ALLS L -- 4.3 Positive Results for ALLL S -- 4.4 Other Negative Results -- 5 Mining Under Homeomorphism and Minor Embedding -- 6 Conclusions and Future Work -- References -- Processing Markov Logic Networks with GPUs: Accelerating Network Grounding -- 1 Introduction -- 2 Markov Logic, Tuffy, Datalog and GPUs -- 2.1 Inference in Markov Logic -- 2.2 Optimizations -- 2.3 Learning -- 2.4 Tuffy -- 2.5 Evaluation of Datalog Programs -- 2.6 GPU Architecture and Programming -- 3 Our GPU-Based Markov Logic Platform -- 4 Experimental Evaluation -- 4.1 Applications and Hardware-Software Platform -- 4.2 Results -- 5 Related Work -- 6 Conclusions -- References -- Using ILP to Identify Pathway Activation Patterns in Systems Biology -- 1 Introduction and Background -- 2 Overview of Propositionalization -- 3 Methods -- 3.1 Raw Data -- 3.2 Data Processing -- 3.3 Searching for Pathway Activation Patterns -- 4 Results -- 4.1 Quantitative Evaluation and Comparison with SBV Improver Model -- 4.2 Results for Warmr Method. 4.3 Results for Warmr/TreeLiker Combined Method -- 5 Conclusions -- References -- kProbLog: An Algebraic Prolog for Kernel Programming -- 1 Introduction -- 2 KProbLogS -- 3 kProbLog -- 3.1 Recursive kProbLog Program with Meta-Functions -- 3.2 The Jacobi Method -- 3.3 kProbLog TP-Operator with Meta-Functions -- 4 kProbLogS[x] -- 4.1 Polynomials for Feature Extraction -- 4.2 The @id Meta-Function -- 5 Graph Kernels -- 5.1 Weisfeiler-Lehman Graph Kernel and Propagation Kernels -- 5.2 Graph Invariant Kernels -- 6 Conclusions -- References -- An Exercise in Declarative Modeling for Relational Query Mining -- 1 Introduction -- 2 Problem Statement -- 3 Encoding -- 4 First Order Model -- 5 Experiments -- 6 Model Discussion and Generalization -- 7 Related Work -- 8 Conclusions -- A Appendix: Introduction to IDP -- References -- Learning Inference by Induction -- 1 Introduction -- 2 Learning Logical Inference -- 2.1 Learning Logics -- 2.2 Learning from 1-Step Transitions -- 2.3 Learning Deduction Rules by LF1T -- 3 Learning Non-logical Inference Rules -- 3.1 Abduction -- 3.2 Frame Axiom -- 3.3 Conversational Implicature -- 4 Discussion -- 5 Conclusion -- References -- Identification of Transition Models of Biological Systems in the Presence of Transition Noise -- 1 Introduction -- 2 Transition Identification Under Transition Noise -- 3 Empirical Evaluation -- 3.1 Problems -- 3.2 Data -- 3.3 Models -- 3.4 Algorithms and Machines -- 3.5 Method -- 3.6 Results -- 3.7 Transition Identification Worked Example: Water -- 4 Related Work -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996466006103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers / / edited by Jesse Davis, Jan Ramon
| Inductive Logic Programming [[electronic resource] ] : 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers / / edited by Jesse Davis, Jan Ramon |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (X, 211 p. 62 illus. in color.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Application software Computer logic Computers Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Information Systems Applications (incl. Internet) Logics and Meanings of Programs Computation by Abstract Devices |
| ISBN | 3-319-23708-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Reframing on Relational Data -- Inductive Learning using Constraint-driven Bias -- Nonmonotonic Learning in Large Biological Networks -- Construction of Complex Aggregates with Random Restart Hill-Climbing -- Logical minimisation of meta-rules within Meta-Interpretive Learning -- Goal and plan recognition via parse trees using prefix and infix probability computation -- Effectively creating weakly labeled training examples via approximate domain knowledge -- Learning Prime Implicant Conditions From Interpretation Transition -- Statistical Relational Learning for Handwriting Recognition -- The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions -- Towards machine learning of predictive models from ecological data -- PageRank, ProPPR, and Stochastic Logic Programs -- Complex aggregates over clusters of elements -- On the Complexity of Frequent Subtree Mining in Very Simple Structures. |
| Record Nr. | UNISA-996466459403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers / / edited by Jesse Davis, Jan Ramon
| Inductive Logic Programming [[electronic resource] ] : 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers / / edited by Jesse Davis, Jan Ramon |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (X, 211 p. 62 illus. in color.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Application software Computer logic Computers Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Information Systems Applications (incl. Internet) Logics and Meanings of Programs Computation by Abstract Devices |
| ISBN | 3-319-23708-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Reframing on Relational Data -- Inductive Learning using Constraint-driven Bias -- Nonmonotonic Learning in Large Biological Networks -- Construction of Complex Aggregates with Random Restart Hill-Climbing -- Logical minimisation of meta-rules within Meta-Interpretive Learning -- Goal and plan recognition via parse trees using prefix and infix probability computation -- Effectively creating weakly labeled training examples via approximate domain knowledge -- Learning Prime Implicant Conditions From Interpretation Transition -- Statistical Relational Learning for Handwriting Recognition -- The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions -- Towards machine learning of predictive models from ecological data -- PageRank, ProPPR, and Stochastic Logic Programs -- Complex aggregates over clusters of elements -- On the Complexity of Frequent Subtree Mining in Very Simple Structures. |
| Record Nr. | UNINA-9910484759403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes
| Inductive Logic Programming [[electronic resource] ] : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XIII, 141 p. 31 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Computers Application software Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Computation by Abstract Devices Information Systems Applications (incl. Internet) |
| ISBN | 3-662-44923-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement -- 1 Introduction -- 1.1 Bayesian MIL Versus Probabilistic ILP -- 1.2 Multiple and Single Models -- 2 MetaBayes Refinement Framework -- 2.1 Setting -- 2.2 Generalised Meta-Interpreter -- 2.3 Stochastic Refinement -- 2.4 Prior, Likelihood and Posterior -- 3 Implementation -- 3.1 MetaBayes -- 3.2 MetaBayesMAP -- 3.3 MetaBayesSiLP -- 3.4 MilProbLog -- 4 Experiments -- 4.1 Binary Prediction - MetaBayes vs. MetaMap -- 4.2 Probabilistic Prediction - MetaBayes vs. MetaBayesSiLP vs. MilProbLog -- 5 Related Work -- 6 Conclusion and Further Work -- References -- On Differentially Private Inductive Logic Programming -- 1 Introduction -- 2 Preliminaries -- 2.1 Inductive Logic Programming -- 2.2 Differential Privacy -- 3 Problem Formulation -- 4 Trade-Off on Privacy and Utility -- 4.1 Our Utility Model -- 4.2 A Lower Bound on Privacy Parameter -- 5 Differentially Private ILP Algorithm -- 5.1 A Non-private ILP Algorithm -- 5.2 A Differentially Private Selection Algorithm -- 5.3 A Differentially Private Reduction Algorithm -- 5.4 Our Differentially Private ILP Algorithm -- 6 Experiment -- 7 Conclusion -- References -- Learning Through Hypothesis Refinement Using Answer Set Programming -- 1 Introduction -- 2 Background -- 2.1 Top-Directed Abductive Learning in ASP -- 3 Learning Through Hypothesis Refinement -- 3.1 Hypothesis Refinement -- 3.2 Learning a Partial Hypothesis -- 4 RASPAL: Iterative Learning by Refinement -- 4.1 Algorithms -- 5 Experiment -- 6 Conclusion and Future Work -- References -- A BDD-Based Algorithm for Learning from Interpretation Transition -- 1 Introduction -- 2 Learning from 1-Step Transitions -- 3 BDD Algorithms for LF1T -- 4 Experiments -- 5 Conclusion and Future Work -- A Appendix.
A.1 Proof of Theorem 1 -- References -- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization -- 1 Introduction -- 2 Background -- 3 Relational Imitation Learning -- 4 Relational Transfer -- 5 Experiments -- 6 Discussion and Conclusion -- References -- A Direct Policy-Search Algorithm for Relational Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Terminology -- 3.1 Blocks World -- 4 CERRLA Algorithm -- 4.1 Cross-Entropy Method -- 4.2 Rule Discovery -- 4.3 Policy-Search Process -- 5 Evaluation -- 5.1 Blocks World -- 5.2 Ms.Pac-Man -- 5.3 Carcassonne -- 6 Conclusions -- References -- AND Parallelism for ILP: The APIS System -- 1 Introduction -- 2 Background -- 2.1 Parallel Execution of Logic Programs -- 3 The APIS System -- 3.1 Redundancy Avoidance -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results and Discussion -- 5 Parallel Execution of ILP Systems -- 6 Conclusions -- References -- Generalized Counting for Lifted Variable Elimination -- 1 Introduction -- 2 Representation -- 3 Lifted Variable Elimination -- 4 Generalized Counting Formulas -- 4.1 A Motivating Example -- 4.2 Definition -- 5 Conversion Operations -- 5.1 Counting Conversion -- 5.2 Merging Counting Formulas -- 5.3 Merge-Counting -- 6 Elimination Operations -- 6.1 Sum-out by Counting -- 6.2 Aggregation -- 7 Relation to Joint Conversion -- 8 Conclusion -- References -- A FOIL-Like Method for Learning under Incompleteness and Vagueness -- 1 Introduction -- 2 Preliminaries -- 3 Learning Fuzzy EL(D) Axioms -- 3.1 The Problem Statement -- 3.2 The Solution Strategy -- 4 Related Work -- 5 Towards an Application in Tourism -- 6 Conclusions -- References -- Author Index. |
| Record Nr. | UNINA-9910484814403321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive Logic Programming [[electronic resource] ] : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes
| Inductive Logic Programming [[electronic resource] ] : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / / edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XIII, 141 p. 31 illus.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Mathematical logic
Artificial intelligence Computer programming Computer logic Computers Application software Mathematical Logic and Formal Languages Artificial Intelligence Programming Techniques Logics and Meanings of Programs Computation by Abstract Devices Information Systems Applications (incl. Internet) |
| ISBN | 3-662-44923-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement -- 1 Introduction -- 1.1 Bayesian MIL Versus Probabilistic ILP -- 1.2 Multiple and Single Models -- 2 MetaBayes Refinement Framework -- 2.1 Setting -- 2.2 Generalised Meta-Interpreter -- 2.3 Stochastic Refinement -- 2.4 Prior, Likelihood and Posterior -- 3 Implementation -- 3.1 MetaBayes -- 3.2 MetaBayesMAP -- 3.3 MetaBayesSiLP -- 3.4 MilProbLog -- 4 Experiments -- 4.1 Binary Prediction - MetaBayes vs. MetaMap -- 4.2 Probabilistic Prediction - MetaBayes vs. MetaBayesSiLP vs. MilProbLog -- 5 Related Work -- 6 Conclusion and Further Work -- References -- On Differentially Private Inductive Logic Programming -- 1 Introduction -- 2 Preliminaries -- 2.1 Inductive Logic Programming -- 2.2 Differential Privacy -- 3 Problem Formulation -- 4 Trade-Off on Privacy and Utility -- 4.1 Our Utility Model -- 4.2 A Lower Bound on Privacy Parameter -- 5 Differentially Private ILP Algorithm -- 5.1 A Non-private ILP Algorithm -- 5.2 A Differentially Private Selection Algorithm -- 5.3 A Differentially Private Reduction Algorithm -- 5.4 Our Differentially Private ILP Algorithm -- 6 Experiment -- 7 Conclusion -- References -- Learning Through Hypothesis Refinement Using Answer Set Programming -- 1 Introduction -- 2 Background -- 2.1 Top-Directed Abductive Learning in ASP -- 3 Learning Through Hypothesis Refinement -- 3.1 Hypothesis Refinement -- 3.2 Learning a Partial Hypothesis -- 4 RASPAL: Iterative Learning by Refinement -- 4.1 Algorithms -- 5 Experiment -- 6 Conclusion and Future Work -- References -- A BDD-Based Algorithm for Learning from Interpretation Transition -- 1 Introduction -- 2 Learning from 1-Step Transitions -- 3 BDD Algorithms for LF1T -- 4 Experiments -- 5 Conclusion and Future Work -- A Appendix.
A.1 Proof of Theorem 1 -- References -- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization -- 1 Introduction -- 2 Background -- 3 Relational Imitation Learning -- 4 Relational Transfer -- 5 Experiments -- 6 Discussion and Conclusion -- References -- A Direct Policy-Search Algorithm for Relational Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Terminology -- 3.1 Blocks World -- 4 CERRLA Algorithm -- 4.1 Cross-Entropy Method -- 4.2 Rule Discovery -- 4.3 Policy-Search Process -- 5 Evaluation -- 5.1 Blocks World -- 5.2 Ms.Pac-Man -- 5.3 Carcassonne -- 6 Conclusions -- References -- AND Parallelism for ILP: The APIS System -- 1 Introduction -- 2 Background -- 2.1 Parallel Execution of Logic Programs -- 3 The APIS System -- 3.1 Redundancy Avoidance -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results and Discussion -- 5 Parallel Execution of ILP Systems -- 6 Conclusions -- References -- Generalized Counting for Lifted Variable Elimination -- 1 Introduction -- 2 Representation -- 3 Lifted Variable Elimination -- 4 Generalized Counting Formulas -- 4.1 A Motivating Example -- 4.2 Definition -- 5 Conversion Operations -- 5.1 Counting Conversion -- 5.2 Merging Counting Formulas -- 5.3 Merge-Counting -- 6 Elimination Operations -- 6.1 Sum-out by Counting -- 6.2 Aggregation -- 7 Relation to Joint Conversion -- 8 Conclusion -- References -- A FOIL-Like Method for Learning under Incompleteness and Vagueness -- 1 Introduction -- 2 Preliminaries -- 3 Learning Fuzzy EL(D) Axioms -- 3.1 The Problem Statement -- 3.2 The Solution Strategy -- 4 Related Work -- 5 Towards an Application in Tourism -- 6 Conclusions -- References -- Author Index. |
| Record Nr. | UNISA-996199679503316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive logic programming : 18th international conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008 : proceedings / / Filip Železný, Nada Lavrač (editors)
| Inductive logic programming : 18th international conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008 : proceedings / / Filip Železný, Nada Lavrač (editors) |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg ; ; New York : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (X, 358 p.) |
| Disciplina | 005.115 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Logic programming |
| ISBN | 3-540-85928-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Building Theories of the World: Human and Machine Learning Perspectives -- SRL without Tears: An ILP Perspective -- Semantic Web Meets ILP: Unconsumated Love, or No Love Lost? -- Learning Expressive Models of Gene Regulation -- Information Overload and FP7 Funding Opportunities in 2009-10 -- Research Papers -- A Model to Study Phase Transition and Plateaus in Relational Learning -- Top-Down Induction of Relational Model Trees in Multi-instance Learning -- Challenges in Relational Learning for Real-Time Systems Applications -- Discriminative Structure Learning of Markov Logic Networks -- An Experiment in Robot Discovery with ILP -- Using the Bottom Clause and Mode Declarations on FOL Theory Revision from Examples -- DL-FOIL Concept Learning in Description Logics -- Feature Discovery with Type Extension Trees -- Feature Construction Using Theory-Guided Sampling and Randomised Search -- Foundations of Onto-Relational Learning -- L-Modified ILP Evaluation Functions for Positive-Only Biological Grammar Learning -- Logical Hierarchical Hidden Markov Models for Modeling User Activities -- Learning with Kernels in Description Logics -- Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms -- A Comparison between Two Statistical Relational Models -- Brave Induction -- A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases -- A Note on Refinement Operators for IE-Based ILP Systems -- Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach -- Learning Block-Preserving Outerplanar Graph Patterns and Its Application to Data Mining. |
| Record Nr. | UNISA-996466246203316 |
| Berlin ; ; Heidelberg ; ; New York : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Inductive logic programming : 18th international conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008 : proceedings / / Filip Železný, Nada Lavrač (editors)
| Inductive logic programming : 18th international conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008 : proceedings / / Filip Železný, Nada Lavrač (editors) |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg ; ; New York : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (X, 358 p.) |
| Disciplina | 005.115 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Logic programming |
| ISBN | 3-540-85928-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Building Theories of the World: Human and Machine Learning Perspectives -- SRL without Tears: An ILP Perspective -- Semantic Web Meets ILP: Unconsumated Love, or No Love Lost? -- Learning Expressive Models of Gene Regulation -- Information Overload and FP7 Funding Opportunities in 2009-10 -- Research Papers -- A Model to Study Phase Transition and Plateaus in Relational Learning -- Top-Down Induction of Relational Model Trees in Multi-instance Learning -- Challenges in Relational Learning for Real-Time Systems Applications -- Discriminative Structure Learning of Markov Logic Networks -- An Experiment in Robot Discovery with ILP -- Using the Bottom Clause and Mode Declarations on FOL Theory Revision from Examples -- DL-FOIL Concept Learning in Description Logics -- Feature Discovery with Type Extension Trees -- Feature Construction Using Theory-Guided Sampling and Randomised Search -- Foundations of Onto-Relational Learning -- L-Modified ILP Evaluation Functions for Positive-Only Biological Grammar Learning -- Logical Hierarchical Hidden Markov Models for Modeling User Activities -- Learning with Kernels in Description Logics -- Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms -- A Comparison between Two Statistical Relational Models -- Brave Induction -- A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases -- A Note on Refinement Operators for IE-Based ILP Systems -- Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach -- Learning Block-Preserving Outerplanar Graph Patterns and Its Application to Data Mining. |
| Record Nr. | UNINA-9910767517603321 |
| Berlin ; ; Heidelberg ; ; New York : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive logic programming : 16th international conference, ilp 2006, santiago de compostela, spain, august 24-27, 2006, revised selected papers / / edited by Stephen Muggleton, Ramon Otero, Alireza Tamaddoni-Nezhad
| Inductive logic programming : 16th international conference, ilp 2006, santiago de compostela, spain, august 24-27, 2006, revised selected papers / / edited by Stephen Muggleton, Ramon Otero, Alireza Tamaddoni-Nezhad |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, United States : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (XII, 456 p.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico | Logic programming |
| ISBN | 3-540-73847-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- Actions, Causation and Logic Programming -- Challenges to Machine Learning: Relations Between Reality and Appearance -- First-Order Probabilistic Languages: Into the Unknown -- Integration of Learning and Reasoning Techniques -- Injecting Life with Computers -- Special Issue Extended Abstracts -- On the Connection Between the Phase Transition of the Covering Test and the Learning Success Rate -- Revising Probabilistic Prolog Programs -- Inductive Logic Programming for Gene Regulation Prediction -- QG/GA: A Stochastic Search for Progol -- Generalized Ordering-Search for Learning Directed Probabilistic Logical Models -- ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctions -- Margin-Based First-Order Rule Learning -- Research Papers -- Extension of the Top-Down Data-Driven Strategy to ILP -- Extracting Requirements from Scenarios with ILP -- Learning Recursive Patterns for Biomedical Information Extraction -- Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks -- Multi-class Prediction Using Stochastic Logic Programs -- Structuring Natural Language Data by Learning Rewriting Rules -- An Efficient Algorithm for Computing Kernel Function Defined with Anti-unification -- Towards Automating Simulation-Based Design Verification Using ILP -- Minimal Distance-Based Generalisation Operators for First-Order Objects -- Efficient and Scalable Induction of Logic Programs Using a Deductive Database System -- Inductive Mercury Programming -- An ILP Refinement Operator for Biological Grammar Learning -- Combining Macro-operators with Control Knowledge -- Frequent Hypergraph Mining -- Induction of Fuzzy and Annotated Logic Programs -- Boosting Descriptive ILP for Predictive Learning in Bioinformatics -- Relational Sequence Alignments and Logos -- On the Missing Link Between Frequent Pattern Discovery and Concept Formation -- Learning Modal Theories -- A Mining Algorithm Using Property Items Extracted from Sampled Examples -- The Complexity of Translating BLPs to RMMs -- Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming -- ILP Through Propositionalization and Stochastic k-Term DNF Learning -- ?-Subsumption Based on Object Context -- Word Sense Disambiguation Using Inductive Logic Programming -- ReMauve: A Relational Model Tree Learner -- Relational Data Mining Applied to Virtual Engineering of Product Designs. |
| Record Nr. | UNINA-9910483505003321 |
| Berlin, Germany ; ; New York, United States : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inductive logic programming : 16th international conference, ilp 2006, santiago de compostela, spain, august 24-27, 2006, revised selected papers / / edited by Stephen Muggleton, Ramon Otero, Alireza Tamaddoni-Nezhad
| Inductive logic programming : 16th international conference, ilp 2006, santiago de compostela, spain, august 24-27, 2006, revised selected papers / / edited by Stephen Muggleton, Ramon Otero, Alireza Tamaddoni-Nezhad |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, United States : , : Springer, , [2007] |
| Descrizione fisica | 1 online resource (XII, 456 p.) |
| Disciplina | 005.115 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico | Logic programming |
| ISBN | 3-540-73847-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- Actions, Causation and Logic Programming -- Challenges to Machine Learning: Relations Between Reality and Appearance -- First-Order Probabilistic Languages: Into the Unknown -- Integration of Learning and Reasoning Techniques -- Injecting Life with Computers -- Special Issue Extended Abstracts -- On the Connection Between the Phase Transition of the Covering Test and the Learning Success Rate -- Revising Probabilistic Prolog Programs -- Inductive Logic Programming for Gene Regulation Prediction -- QG/GA: A Stochastic Search for Progol -- Generalized Ordering-Search for Learning Directed Probabilistic Logical Models -- ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctions -- Margin-Based First-Order Rule Learning -- Research Papers -- Extension of the Top-Down Data-Driven Strategy to ILP -- Extracting Requirements from Scenarios with ILP -- Learning Recursive Patterns for Biomedical Information Extraction -- Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks -- Multi-class Prediction Using Stochastic Logic Programs -- Structuring Natural Language Data by Learning Rewriting Rules -- An Efficient Algorithm for Computing Kernel Function Defined with Anti-unification -- Towards Automating Simulation-Based Design Verification Using ILP -- Minimal Distance-Based Generalisation Operators for First-Order Objects -- Efficient and Scalable Induction of Logic Programs Using a Deductive Database System -- Inductive Mercury Programming -- An ILP Refinement Operator for Biological Grammar Learning -- Combining Macro-operators with Control Knowledge -- Frequent Hypergraph Mining -- Induction of Fuzzy and Annotated Logic Programs -- Boosting Descriptive ILP for Predictive Learning in Bioinformatics -- Relational Sequence Alignments and Logos -- On the Missing Link Between Frequent Pattern Discovery and Concept Formation -- Learning Modal Theories -- A Mining Algorithm Using Property Items Extracted from Sampled Examples -- The Complexity of Translating BLPs to RMMs -- Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming -- ILP Through Propositionalization and Stochastic k-Term DNF Learning -- ?-Subsumption Based on Object Context -- Word Sense Disambiguation Using Inductive Logic Programming -- ReMauve: A Relational Model Tree Learner -- Relational Data Mining Applied to Virtual Engineering of Product Designs. |
| Record Nr. | UNISA-996466244403316 |
| Berlin, Germany ; ; New York, United States : , : Springer, , [2007] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
