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Complex Pattern Mining : New Challenges, Methods and Applications / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| Complex Pattern Mining : New Challenges, Methods and Applications / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (x, 250 pages) : illustrations |
| Disciplina | 006.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Data mining Pattern recognition systems Computational Intelligence Artificial Intelligence Data Mining and Knowledge Discovery Automated Pattern Recognition |
| ISBN | 3-030-36617-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Efficient Infrequent Pattern Mining using Negative Itemset Tree -- Hierarchical Adversarial Training for Multi-Domain -- Optimizing C-index via Gradient Boosting in Medical Survival Analysis -- Order-preserving Biclustering Based on FCA and Pattern Structures -- A text-based regression approach to predict bug-fix time -- A Named Entity Recognition Approach for Albanian Using Deep Learning -- A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining -- Efficient Declarative-based Process Mining using an Enhanced Framework -- Exploiting Pattern Set Dissimilarity for Detecting Changes in Communication Networks -- Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks. |
| Record Nr. | UNINA-9910484953003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken
| Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXVIII, 825 p. 205 illus.) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern recognition Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-46227-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Practical and real-world studies of machine learning, knowledge discovery, data mining -- Innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting -- Recent advances at the frontier of machine learning and data mining with other disciplines. . |
| Record Nr. | UNISA-996466244703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken
| Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXXVI, 817 p. 231 illus.) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern recognition Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-46128-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Practical and real-world studies of machine learning,knowledge discovery, data mining -- Innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting -- Recent advances at the frontier of machine learning and data mining with other disciplines. |
| Record Nr. | UNISA-996465981903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken
| Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXVIII, 825 p. 205 illus.) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern recognition Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-46227-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Practical and real-world studies of machine learning, knowledge discovery, data mining -- Innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting -- Recent advances at the frontier of machine learning and data mining with other disciplines. . |
| Record Nr. | UNINA-9910484632103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken
| Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I / / edited by Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXXVI, 817 p. 231 illus.) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Artificial intelligence Pattern recognition Information storage and retrieval Database management Application software Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition Information Storage and Retrieval Database Management Information Systems Applications (incl. Internet) |
| ISBN | 3-319-46128-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Practical and real-world studies of machine learning,knowledge discovery, data mining -- Innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting -- Recent advances at the frontier of machine learning and data mining with other disciplines. |
| Record Nr. | UNINA-9910484032503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xii, 155 pages) : illustrations |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Computer communication systems Architecture, Computer Application software Education—Data processing Artificial Intelligence Data Mining and Knowledge Discovery Computer Communication Networks Computer System Implementation Computer Applications Computers and Education |
| ISBN | 3-030-48861-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Framework for Pattern Mining and Anomaly Detection in Multi-Dimensional Time Series and Event Logs -- A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality -- Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learners -- Neural Hybrid Recommender: Recommendation Needs Collaboration -- Discovering Discriminative Nodes for Classification with Deep Graph Convolutional Methods -- Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams -- Disentangling Aspect and Opinion Words in Sentiment Analysis Using Lifelong PU Learning -- Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda -- Hough Transform as a Tool for the Classification of Vehicle Speed Changes in on-road Audio Recordings. |
| Record Nr. | UNISA-996418318703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
New Frontiers in Mining Complex Patterns : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras
| New Frontiers in Mining Complex Patterns : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xii, 155 pages) : illustrations |
| Disciplina |
006.3
006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Computer networks Computer systems Application software Education—Data processing Artificial Intelligence Data Mining and Knowledge Discovery Computer Communication Networks Computer System Implementation Computer and Information Systems Applications Computers and Education |
| ISBN | 3-030-48861-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Framework for Pattern Mining and Anomaly Detection in Multi-Dimensional Time Series and Event Logs -- A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality -- Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learners -- Neural Hybrid Recommender: Recommendation Needs Collaboration -- Discovering Discriminative Nodes for Classification with Deep Graph Convolutional Methods -- Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams -- Disentangling Aspect and Opinion Words in Sentiment Analysis Using Lifelong PU Learning -- Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda -- Hough Transform as a Tool for the Classification of Vehicle Speed Changes in on-road Audio Recordings. |
| Record Nr. | UNINA-9910409664803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XII, 197 p. 57 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Arithmetic and logic units, Computer Application software Artificial intelligence Data Mining and Knowledge Discovery Arithmetic and Logic Structures Computer Appl. in Social and Behavioral Sciences Artificial Intelligence |
| ISBN | 3-319-78680-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Learning Association Rules for Pharmacogenomic Studies -- Segment-Removal Based Stuttered Speech Remediation -- Identifying lncRNA-disease Relationships via Heterogeneous Clustering -- Density Estimators for Positive-Unlabeled Learning -- Combinatorial Optimization Algorithms to Mine a Sub-Matrix of Maximal Sum -- A Scaled-Correlation Based Approach for Defining and analyzing functional networks -- Complex Localization in the Multiple Instance Learning Context -- Integrating a Framework for Discovering Alternative App Stores in a Mobile App Monitoring Platform -- Usefulness of Unsupervised Ensemble Learning Methods for Time Series Forecasting of Aggregated or Clustered Load -- Phenotype Prediction with Semi-supervised Classification Trees -- Structuring the Output Space in Multi-label Classification by Using Feature Ranking -- Infinite Mixtures of Markov Chains -- Community-based Semantic Subgroup Discovery. |
| Record Nr. | UNISA-996465525603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
New Frontiers in Mining Complex Patterns : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| New Frontiers in Mining Complex Patterns : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XII, 197 p. 57 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Computer arithmetic and logic units Social sciences—Data processing Artificial intelligence Data Mining and Knowledge Discovery Arithmetic and Logic Structures Computer Application in Social and Behavioral Sciences Artificial Intelligence |
| ISBN | 3-319-78680-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Learning Association Rules for Pharmacogenomic Studies -- Segment-Removal Based Stuttered Speech Remediation -- Identifying lncRNA-disease Relationships via Heterogeneous Clustering -- Density Estimators for Positive-Unlabeled Learning -- Combinatorial Optimization Algorithms to Mine a Sub-Matrix of Maximal Sum -- A Scaled-Correlation Based Approach for Defining and analyzing functional networks -- Complex Localization in the Multiple Instance Learning Context -- Integrating a Framework for Discovering Alternative App Stores in a Mobile App Monitoring Platform -- Usefulness of Unsupervised Ensemble Learning Methods for Time Series Forecasting of Aggregated or Clustered Load -- Phenotype Prediction with Semi-supervised Classification Trees -- Structuring the Output Space in Multi-label Classification by Using Feature Ranking -- Infinite Mixtures of Markov Chains -- Community-based Semantic Subgroup Discovery. |
| Record Nr. | UNINA-9910349425303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 239 p. 57 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Database management Information storage and retrieval Artificial intelligence Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Artificial Intelligence |
| ISBN | 3-319-39315-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- New Frontiers in Mining Complex Patterns (NFMCP 2015) -- Organization -- Contents -- Data Stream Mining -- Adaptive Ensembles for Evolving Data Streams -- Combining Block-Based and Online Solutions -- 1 Introduction -- 2 Concept Drift in Data Streams -- 3 Ensembles for Evolving Data Streams -- 4 AUE and OAUE Ensembles -- 4.1 Accuracy Updated Ensemble -- 4.2 Online Accuracy Updated Ensemble -- 5 Final Remarks and Open Issues -- References -- Comparison of Tree-Based Methods for Multi-target Regression on Data Streams -- 1 Introduction -- 2 Background and Related Work -- 2.1 Multi-target Regression -- 2.2 Data Streams -- 2.3 Multi-target Regression on Data Streams -- 3 Tree-Based Approaches for Multi-target Regression on Data Streams -- 3.1 A Local Approach to MTR -- 3.2 A Global Approach to MTR -- 3.3 Ensemble of Trees for MTR on Data Streams -- 3.4 Baseline Method -- 4 Experimental Setup -- 4.1 Experimental Questions -- 4.2 Evaluation Measures and Experimental Methodology -- 4.3 Datasets -- 4.4 Compared Methods -- 5 Results -- 5.1 Predictive Performance (RMAE) -- 5.2 Time Consumption -- 5.3 Memory Consumption -- 6 Conclusions and Further Work -- References -- Frequent Itemsets Mining in Data Streams Using Reconfigurable Hardware -- 1 Introduction -- 2 Theoretical Basis -- 2.1 Reconfigurable Computing -- 3 Related Works -- 4 A Method for Frequent Itemsets Mining Using Reconfigurable Hardware -- 4.1 Frequent 1-Itemsets Detection -- 4.2 Proposed Method -- 5 Results -- 5.1 Discussion -- 6 Conclusions -- References -- Discovering and Tracking Organizational Structures in Event Logs -- 1 Introduction -- 2 Basics -- 3 Time-Evolving Organization Structure Tracker -- 3.1 Resource Community Detection -- 3.2 Tracking Evolutions of Resource Communities -- 4 Case Studies -- 5 Conclusions -- References.
Intelligent Adaptive Ensembles for Data Stream Mining: A High Return on Investment Approach -- 1 Introduction -- 2 Background -- 2.1 Size of Ensemble Object in Memory -- 2.2 Ensemble Size versus Utility -- 3 Adaptive Ensemble Size Algorithm -- 4 Experimentation -- 4.1 Results -- 5 Conclusion -- References -- Mining Periodic Changes in Complex Dynamic Data Through Relational Pattern Discovery -- 1 Introduction -- 2 Basics and Definitions -- 3 The Method -- 3.1 Relational Frequent Pattern Discovery -- 3.2 Emerging Pattern Extraction -- 3.3 Periodic Change Detection -- 4 Experiments -- 5 Related Works -- 6 Conclusions -- References -- Classification -- The Usefulness of Roughly Balanced Bagging for Complex and High-Dimensional Imbalanced Data -- 1 Introduction -- 2 Related Works -- 3 Studying the Role of Components in Roughly Balanced Bagging -- 3.1 Choosing Algorithms to Learn Component Classifiers -- 3.2 The Influence of the Number of Component Classifiers -- 3.3 Diversity of Component Classifiers -- 4 Influence of the Type of Examples -- 5 Applying a Random Selection of Attributes -- 6 Discussion and Final Remarks -- References -- Classifying Traces of Event Logs on the Basis of Security Risks -- 1 Introduction -- 2 Preliminaries -- 3 The Classification Problem and Our Approach for Solving It -- 3.1 The Challenges of Evaluating a Classification and Our Solution -- 4 The Monte Carlo Classification Algorithm -- 5 Experimental Validation -- 6 Related Work -- 7 Conclusions and Future Work -- References -- Redescription Mining with Multi-target Predictive Clustering Trees -- 1 Introduction -- 2 Notation and Definitions -- 3 The CLUS-RM Algorithm -- 3.1 The Procedure for Creating Redescriptions -- 3.2 Rule Size Minimization -- 3.3 Algorithm Time Complexity -- 4 Mining Redescriptions on Data Describing Countries -- 5 Algorithm Evaluation and Comparison. 6 Conclusion -- A Appendix -- References -- Mining Complex Data -- Generalizing Patterns for Cross-Domain Analogy -- 1 Introduction -- 2 Preliminaries -- 3 Analogy and Inference -- 3.1 Representation Formalism -- 3.2 Analogy -- 3.3 Inference and Re-representation -- 4 Analogical Pattern Generalization -- 4.1 Formal Definition -- 4.2 Evaluation -- 4.3 Addition and Union -- 4.4 Military and Medical Strategy -- 4.5 Patterns Assessment -- 5 Conclusions -- References -- Spectral Features for Audio Based Vehicle Identification -- 1 Introduction -- 1.1 Related Work -- 1.2 Vehicle Classes -- 2 Data Collection and Description -- 3 Feature Set -- 3.1 Feature Selection -- 4 Experiments -- 4.1 Classifiers -- 4.2 Data -- 4.3 Classification Results -- 4.4 Hierarchical Classification -- 5 Summary and Conclusions -- References -- Probabilistic Frequent Subtree Kernels -- 1 Introduction -- 2 The Probabilistic Frequent Subtree Kernel -- 2.1 Probabilistic Frequent Subtrees -- 2.2 Implementation Issues and Runtime Analysis -- 3 Experiments -- 3.1 Datasets -- 3.2 Runtime -- 3.3 Recall -- 3.4 Stability of Probabilistic Subtree Patterns -- 3.5 Predictive Performance -- 4 Conclusion and Future Work -- References -- Heterogeneous Network Decomposition and Weighting with Text Mining Heuristics -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Network Decomposition -- 3.2 Classification -- 4 Methodology Improvement -- 4.1 Imbalanced Data Sets and Label Propagation -- 4.2 Text Mining Inspired Weights Calculation -- 5 Experimental Setting and Results -- 5.1 Data Set Description -- 5.2 Experiment Description -- 5.3 Experimental Results -- 6 Conclusions and Further Work -- References -- Sequences -- Semi-supervised Multivariate Sequential Pattern Mining -- 1 Introduction -- 2 Related Work -- 3 The Semi-supervised Learning Framework -- 3.1 Graph Construction. 3.2 Label Propagation -- 3.3 Extension to Out-of-Samples -- 4 Experimental Analysis -- 5 Conclusion -- References -- Evaluating a Simple String Representation for Intra-day Foreign Exchange Prediction -- 1 Introduction -- 2 Previous Work -- 3 Simple Strategy -- 4 String Subsequences Strategy -- 4.1 n-Grams -- 4.2 Time Decay n-Grams -- 5 Experiments -- 5.1 Simple String Strategy: Word vs. Alphabet Length -- 5.2 Parzen Window Strategy: Regression vs. Classification -- 5.3 Simple Strings Strategy vs. SVM Classification -- 5.4 Time Decay n-Grams: Decay Analysis -- 5.5 Simple String Strategy vs. Time Decay n-Grams -- 6 Conclusions -- References -- Author Index. |
| Record Nr. | UNISA-996465681503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
