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Advanced Data Mining and Applications [[electronic resource] ] : 5th International Conference, ADMA 2009, Chengdu, China, August 17-19, 2009, Proceedings / / edited by Ronghuai Huang, Qiang Yang, Jian Pei, João Gama, Xiaofeng Meng, Xue Li
| Advanced Data Mining and Applications [[electronic resource] ] : 5th International Conference, ADMA 2009, Chengdu, China, August 17-19, 2009, Proceedings / / edited by Ronghuai Huang, Qiang Yang, Jian Pei, João Gama, Xiaofeng Meng, Xue Li |
| Edizione | [1st ed. 2009.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009 |
| Descrizione fisica | 1 online resource (XXI, 807 p.) |
| Disciplina | 004n/a |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Information storage and retrieval Computers Application software Pattern recognition Artificial intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl. Internet) Pattern Recognition Artificial Intelligence |
| Soggetto genere / forma |
Kongress.
Peking (2009) |
| ISBN |
1-280-38316-X
9786613561084 3-642-03348-2 |
| Classificazione |
DAT 600f
DAT 703f DAT 825f SS 4800 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynotes -- Regular Papers -- Short Papers. |
| Record Nr. | UNISA-996465765803316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Intelligent Data Analysis X [[electronic resource] ] : 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011, Proceedings / / edited by João Gama, Elizabeth Bradley, Jaakko Hollmen
| Advances in Intelligent Data Analysis X [[electronic resource] ] : 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011, Proceedings / / edited by João Gama, Elizabeth Bradley, Jaakko Hollmen |
| Edizione | [1st ed. 2011.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 |
| Descrizione fisica | 1 online resource (XIV, 425 p. 144 illus.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Application software Artificial intelligence Information storage and retrieval Algorithms Data mining Database Management Information Systems Applications (incl. Internet) Artificial Intelligence Information Storage and Retrieval Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery |
| ISBN | 3-642-24800-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465963203316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Discovery Science [[electronic resource] ] : 26th International Conference, DS 2023, Porto, Portugal, October 9–11, 2023, Proceedings / / edited by Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, João Gama, Pedro H. Abreu
| Discovery Science [[electronic resource] ] : 26th International Conference, DS 2023, Porto, Portugal, October 9–11, 2023, Proceedings / / edited by Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, João Gama, Pedro H. Abreu |
| Autore | Bifet Albert |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (725 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
LorenaAna Carolina
RibeiroRita P GamaJoão AbreuPedro H |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Education - Data processing Data mining Application software Social sciences - Data processing Image processing - Digital techniques Computer vision Artificial Intelligence Computers and Education Data Mining and Knowledge Discovery Computer and Information Systems Applications Computer Application in Social and Behavioral Sciences Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-45275-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Ensembles of classifiers and quantifiers with data fusion for Quantification Learning -- Exploring the Intricacies of Neural Network Optimization -- Exploring the Reduction of Configuration Spaces of Workflows -- iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression -- Knowledge-Guided Additive Modeling For Supervised Regression -- Audience Prediction for Game Streaming Channels Based on Vectorization of User Comments -- From Tweets to Stance: An Unsupervised Framework for User Stance Detection on Twitter -- GLORIA: A Graph Convolutional Network-based Approach for Review Spam Detection -- Unmasking COVID-19 False Information on Twitter: a Topic-based Approach with BERT -- Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers -- Counterfactuals Explanations for Outliers via Subspaces Density Contrastive Loss -- Explainable Spatio-Temporal Graph Modeling -- Probabilistic Scoring Lists for Interpretable Machine Learning -- Refining Temporal Visualizations Using the Directional Coherence Loss -- Semantic enrichment of explanations of AI models for healthcare -- Text to Time Series Representations: Towards Interpretable Predictive Models -- Enhancing intra-modal similarity in a cross-modal triplet loss -- Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy -- Extrapolation is Not the Same as Interpolation -- Gene Interactions in Survival Data Analysis: A Data-driven Approach Using Restricted Mean Survival Time and Literature Mining -- Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features -- EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories -- Fairness-aware Mixture of Experts with Interpretability Budgets -- GenFair: A Genetic Fairness-Enhancing Data Generation Framework -- Privacy-Preserving Learning of Random Forests Without Revealing the Trees -- Unlearning Spurious Correlations in Chest X-ray Classification -- Explaining the Chronological Attribution of Greek Papyri Images -- Leveraging the Spatiotemporal Analysis of Meisho-e Landscapes -- Predictive Inference Model of the Physical Environment that emulates Predictive Coding -- Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains -- Which Way to Go - Finding Frequent Trajectories Through Clustering -- Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks -- Interpretable Data Partitioning through Tree-based Clustering Methods -- Jaccard-constrained dense subgraph discovery -- RIMBO - an ontology for model revision databases -- Unsupervised Graph Neural Networks for Source Code Similarity Detection -- A Universal Approach for Post-Correcting Time Series -- Forecasts: Reducing Long-term Errors In Multistep Scenarios -- Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting -- Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes -- Chance and the predictive limit in basketball (both college and professional) -- Exploring Label Correlations for Quantification of ICD Codes -- LGEM+: a first-order logic framework for automated improvement of metabolic network models through abduction -- Predicting age from human lung tissue through multi-modal data integration -- Error Analysis on Industry Data:Using Weak Segment Detection for Local Model Agnostic Prediction Intervals -- HEART: Heterogeneous Log Anomaly Detection using Robust Transformers -- Multi-Kernel Time Series Outlier Detection -- Toward Streamlining the Evaluation of Novelty Detection in Data Streams. |
| Record Nr. | UNINA-9910747595603321 |
Bifet Albert
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discovery Science [[electronic resource] ] : 26th International Conference, DS 2023, Porto, Portugal, October 9–11, 2023, Proceedings / / edited by Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, João Gama, Pedro H. Abreu
| Discovery Science [[electronic resource] ] : 26th International Conference, DS 2023, Porto, Portugal, October 9–11, 2023, Proceedings / / edited by Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, João Gama, Pedro H. Abreu |
| Autore | Bifet Albert |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (725 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
LorenaAna Carolina
RibeiroRita P GamaJoão AbreuPedro H |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Education - Data processing Data mining Application software Social sciences - Data processing Image processing - Digital techniques Computer vision Artificial Intelligence Computers and Education Data Mining and Knowledge Discovery Computer and Information Systems Applications Computer Application in Social and Behavioral Sciences Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-45275-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Ensembles of classifiers and quantifiers with data fusion for Quantification Learning -- Exploring the Intricacies of Neural Network Optimization -- Exploring the Reduction of Configuration Spaces of Workflows -- iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression -- Knowledge-Guided Additive Modeling For Supervised Regression -- Audience Prediction for Game Streaming Channels Based on Vectorization of User Comments -- From Tweets to Stance: An Unsupervised Framework for User Stance Detection on Twitter -- GLORIA: A Graph Convolutional Network-based Approach for Review Spam Detection -- Unmasking COVID-19 False Information on Twitter: a Topic-based Approach with BERT -- Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers -- Counterfactuals Explanations for Outliers via Subspaces Density Contrastive Loss -- Explainable Spatio-Temporal Graph Modeling -- Probabilistic Scoring Lists for Interpretable Machine Learning -- Refining Temporal Visualizations Using the Directional Coherence Loss -- Semantic enrichment of explanations of AI models for healthcare -- Text to Time Series Representations: Towards Interpretable Predictive Models -- Enhancing intra-modal similarity in a cross-modal triplet loss -- Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy -- Extrapolation is Not the Same as Interpolation -- Gene Interactions in Survival Data Analysis: A Data-driven Approach Using Restricted Mean Survival Time and Literature Mining -- Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features -- EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories -- Fairness-aware Mixture of Experts with Interpretability Budgets -- GenFair: A Genetic Fairness-Enhancing Data Generation Framework -- Privacy-Preserving Learning of Random Forests Without Revealing the Trees -- Unlearning Spurious Correlations in Chest X-ray Classification -- Explaining the Chronological Attribution of Greek Papyri Images -- Leveraging the Spatiotemporal Analysis of Meisho-e Landscapes -- Predictive Inference Model of the Physical Environment that emulates Predictive Coding -- Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains -- Which Way to Go - Finding Frequent Trajectories Through Clustering -- Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks -- Interpretable Data Partitioning through Tree-based Clustering Methods -- Jaccard-constrained dense subgraph discovery -- RIMBO - an ontology for model revision databases -- Unsupervised Graph Neural Networks for Source Code Similarity Detection -- A Universal Approach for Post-Correcting Time Series -- Forecasts: Reducing Long-term Errors In Multistep Scenarios -- Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting -- Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes -- Chance and the predictive limit in basketball (both college and professional) -- Exploring Label Correlations for Quantification of ICD Codes -- LGEM+: a first-order logic framework for automated improvement of metabolic network models through abduction -- Predicting age from human lung tissue through multi-modal data integration -- Error Analysis on Industry Data:Using Weak Segment Detection for Local Model Agnostic Prediction Intervals -- HEART: Heterogeneous Log Anomaly Detection using Robust Transformers -- Multi-Kernel Time Series Outlier Detection -- Toward Streamlining the Evaluation of Novelty Detection in Data Streams. |
| Record Nr. | UNISA-996558470803316 |
|
Bifet Albert
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Discovery Science [[electronic resource] ] : 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009 / / edited by João Gama, Vitor Santos Costa, Alipio Jorge, Pavel Brazdil
| Discovery Science [[electronic resource] ] : 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009 / / edited by João Gama, Vitor Santos Costa, Alipio Jorge, Pavel Brazdil |
| Edizione | [1st ed. 2009.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009 |
| Descrizione fisica | 1 online resource (XIII, 474 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer communication systems Data mining Information storage and retrieval Application software User interfaces (Computer systems) Artificial Intelligence Computer Communication Networks Data Mining and Knowledge Discovery Information Storage and Retrieval Information Systems Applications (incl. Internet) User Interfaces and Human Computer Interaction |
| ISBN | 3-642-04747-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Inference and Learning in Planning (Extended Abstract) -- Mining Heterogeneous Information Networks by Exploring the Power of Links -- Learning on the Web -- Learning and Domain Adaptation -- The Two Faces of Active Learning -- An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting -- Detecting New Kinds of Patient Safety Incidents -- Using Data Mining for Wine Quality Assessment -- MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio -- On the Complexity of Constraint-Based Theory Extraction -- Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool -- Regression Trees from Data Streams with Drift Detection -- Mining Frequent Bipartite Episode from Event Sequences -- CHRONICLE: A Two-Stage Density-Based Clustering Algorithm for Dynamic Networks -- Learning Large Margin First Order Decision Lists for Multi-Class Classification -- Centrality Measures from Complex Networks in Active Learning -- Player Modeling for Intelligent Difficulty Adjustment -- Unsupervised Fuzzy Clustering for the Segmentation and Annotation of Upwelling Regions in Sea Surface Temperature Images -- Discovering the Structures of Open Source Programs from Their Developer Mailing Lists -- A Comparison of Community Detection Algorithms on Artificial Networks -- Towards an Ontology of Data Mining Investigations -- OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers -- C-DenStream: Using Domain Knowledge on a Data Stream -- Discovering Influential Nodes for SIS Models in Social Networks -- An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules -- Precision and Recall for Regression -- Mining Local Correlation Patterns in Sets of Sequences -- Subspace Discovery for Promotion: A Cell Clustering Approach -- Contrasting Sequence Groups by Emerging Sequences -- A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams -- A Hybrid Collaborative Filtering System for Contextual Recommendations in Social Networks -- Linear Programming Boosting by Column and Row Generation -- Discovering Abstract Concepts to Aid Cross-Map Transfer for a Learning Agent -- A Dialectic Approach to Problem-Solving -- Gene Functional Annotation with Dynamic Hierarchical Classification Guided by Orthologs -- Stream Clustering of Growing Objects -- Finding the k-Most Abnormal Subgraphs from a Single Graph -- Latent Topic Extraction from Relational Table for Record Matching -- Computing a Comprehensible Model for Spam Filtering -- Better Decomposition Heuristics for the Maximum-Weight Connected Graph Problem Using Betweenness Centrality. |
| Record Nr. | UNISA-996465386403316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
ECML PKDD 2018 Workshops : DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers / / edited by Anna Monreale, Carlos Alzate, Michael Kamp, Yamuna Krishnamurthy, Daniel Paurat, Moamar Sayed-Mouchaweh, Albert Bifet, João Gama, Rita P. Ribeiro
| ECML PKDD 2018 Workshops : DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers / / edited by Anna Monreale, Carlos Alzate, Michael Kamp, Yamuna Krishnamurthy, Daniel Paurat, Moamar Sayed-Mouchaweh, Albert Bifet, João Gama, Rita P. Ribeiro |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (IX, 127 p. 43 illus., 27 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval Computer communication systems Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Computer Communication Networks |
| ISBN | 3-030-14880-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910337569303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge Discovery from Sensor Data [[electronic resource] ] : Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers / / edited by Mohamed Medhat Gaber, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Auroop R. Ganguly
| Knowledge Discovery from Sensor Data [[electronic resource] ] : Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers / / edited by Mohamed Medhat Gaber, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Auroop R. Ganguly |
| Edizione | [1st ed. 2010.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 |
| Descrizione fisica | 1 online resource (IX, 227 p. 110 illus.) |
| Disciplina | 025.04 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Xarxes de sensors
Intel·ligència artificial Information storage and retrieval Computer communication systems Database management Data mining Pattern recognition Information Storage and Retrieval Computer Communication Networks Database Management Data Mining and Knowledge Discovery Pattern Recognition |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
1-280-38628-2
9786613564207 3-642-12519-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned -- Monitoring Incremental Histogram Distribution for Change Detection in Data Streams -- Situation-Aware Adaptive Visualization for Sensory Data Stream Mining -- Unsupervised Plan Detection with Factor Graphs -- WiFi Miner: An Online Apriori-Infrequent Based Wireless Intrusion System -- Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set -- Spatio-temporal Outlier Detection in Precipitation Data -- Large-Scale Inference of Network-Service Disruption upon Natural Disasters -- An Adaptive Sensor Mining Framework for Pervasive Computing Applications -- A Simple Dense Pixel Visualization for Mobile Sensor Data Mining -- Incremental Anomaly Detection Approach for Characterizing Unusual Profiles -- Spatiotemporal Neighborhood Discovery for Sensor Data. |
| Record Nr. | UNISA-996465607203316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares
| Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XLII, 773 p. 198 illus.) |
| Disciplina | 006.312 |
| 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 | 3-319-23525-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996200359603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge
| Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (LVIII, 709 p. 160 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 | 3-319-23528-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Towards Declarative, Domain-OrientedData Analysis -- Sum-Product Networks: Deep Modelswith Tractable Inference -- Mining Online Networks and Communities -- Learning to Acquire Knowledge in a SmartGrid Environment -- Untangling the Web's Invisible Net -- Towards a Digital Time Machine Fueled by BigData and Social Mining -- Abstracts of Journal Track Articles -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Research Track Classification, Regression and Supervised Learning -- Data Split Strategiesfor Evolving Predictive Models -- 1 Introduction -- 2 Data Splits for Model Fitting, Selection,and Assessment -- 3 Issues with Evolving Models -- 4 Data Splits for Evolving Models -- 4.1 Parallel Dump Workflow -- 4.2 Serial Waterfall Workflow -- 4.3 Hybrid Workflow -- 5 Bias Due to Test Set Reuse -- 6 Illustration on Synthetic Data -- 7 Case Study: Paraphrase Detection -- 8 Related Work -- 9 Conclusions -- A Appendix: Bias Due to Test Set Reuse -- References -- Discriminative Interpolation for Classification of Functional Data -- 1 Introduction -- 2 Function Representations and Wavelets -- 3 Related Work -- 4 Classification by Discriminative Interpolation -- 4.1 Training Formulation -- 4.2 Testing Formulation -- 5 Experiments -- 6 Conclusion -- References -- Fast Label Embeddings via Randomized Linear Algebra -- 1 Introduction -- 1.1 Contributions -- 2 Algorithm Derivation -- 2.1 Notation -- 2.2 Background -- 2.3 Rank-Constrained Estimation and Embedding -- 2.4 Rembrandt -- 3 Related Work -- 4 Experiments -- 4.1 ALOI -- 4.2 ODP -- 4.3 LSHTC -- 5 Discussion -- References -- Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation -- 1 Introduction -- 2 General Idea -- 3 Theory -- 4 Closed form Solution for Objective and its Gradient.
5 Experiments -- 6 Conclusions -- References -- Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration -- 1 Introduction -- 2 Proper Scoring Rules -- 2.1 Scoring Rules -- 2.2 Divergence, Entropy and Properness -- 2.3 Expected Loss and Empirical Loss -- 3 Decompositions with Ideal Scores and Calibrated Scores -- 3.1 Ideal Scores Q and the Decomposition L=EL+IL -- 3.2 Calibrated Scores C and the Decomposition L=CL+RL -- 4 Adjusted Scores A and the Decomposition L=AL+PL -- 4.1 Adjustment -- 4.2 The Right Adjustment Procedure Guarantees Decreased Loss -- 5 Decomposition Theorems and Terminology -- 5.1 Decompositions with S,C,Q,Y -- 5.2 Decompositions with S,A,C,Q,Y and Terminology -- 6 Algorithms and Experiments -- 7 Related Work -- 8 Conclusions -- References -- Parameter Learning of Bayesian Network Classifiers Under Computational Constraints -- 1 Introduction -- 2 Related Work -- 3 Background and Notation -- 4 Algorithms for Online Learning of Reduced-Precision Parameters -- 4.1 Learning Maximum Likelihood Parameters -- 4.2 Learning Maximum Margin Parameters -- 5 Experiments -- 5.1 Datasets -- 5.2 Results -- 6 Discussions -- References -- Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning -- 1 Introduction -- 2 Multi-label Classification -- 3 Model Description -- 3.1 Joint Space Embeddings -- 3.2 Learning with Hierarchical Structures Over Labels -- 3.3 Efficient Gradients Computation -- 3.4 Label Ranking to Binary Predictions -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Learning All Labels Together -- 5.2 Learning to Predict Unseen Labels -- 6 Pretrained Label Embeddings as Good Initial Guess -- 6.1 Understanding Label Embeddings -- 6.2 Results -- 7 Conclusions -- Regression with Linear Factored Functions -- 1 Introduction -- 1.1 Kernel Regression. 1.2 Factored Basis Functions -- 2 Regression -- 3 Linear Factored Functions -- 3.1 Function Class -- 3.2 Constraints -- 3.3 Regularization -- 3.4 Optimization -- 4 Empirical Evaluation -- 4.1 Demonstration -- 4.2 Evaluation -- 5 Discussion -- Appendix A LFF Definition and Properties -- Appendix B Inner Loop Derivation -- Appendix C Proofs of the Propositions -- References -- Ridge Regression, Hubness, and Zero-Shot Learning -- 1 Introduction -- 1.1 Background -- 1.2 Research Objective and Contributions -- 2 Zero-Shot Learning as a Regression Problem -- 3 Hubness Phenomenon and the Variance of Data -- 4 Hubness in Regression-Based Zero-Shot Learning -- 4.1 Shrinkage of Projected Objects -- 4.2 Influence of Shrinkage on Nearest Neighbor Search -- 4.3 Additional Argument for Placing Target Objects Closer to the Origin -- 4.4 Summary of the Proposed Approach -- 5 Related Work -- 6 Experiments -- 6.1 Experimental Setups -- 6.2 Task Descriptions and Datasets -- 6.3 Experimental Results -- 7 Conclusion -- References -- Solving Prediction Games with Parallel Batch Gradient Descent -- 1 Introduction -- 2 Problem Setting and Data Transformation Model -- 3 Analysis of Equilibrium Points -- 3.1 Existence of Equilibrium Points -- 3.2 Uniqueness of Equilibrium Points -- 4 Finding the Unique Equilibrium Point Efficiently -- 4.1 Inexact Line Search -- 4.2 Arrow-Hurwicz-Uzawa Method -- 4.3 Parallelized Methods -- 5 Experimental Results -- 5.1 Reference Methods -- 5.2 Performance of the Parameterized Transformation Model -- 5.3 Optimization Algorithms -- 5.4 Parallelized Models -- 6 Conclusion -- References -- Structured Regularizer for Neural Higher-Order Sequence Models -- 1 Introduction -- 2 Related Work -- 3 Higher-Order Conditional Random Fields -- 3.1 Parameter Learning -- 3.2 Forward Algorithm for 2nd-Order CRFs -- 4 Structured Regularizer -- 5 Experiments. 5.1 TIMIT Data Set -- 5.2 Experimental Setup -- 5.3 Labeling Results Using Only MLP Networks -- 5.4 Labeling Results Using LC-CRFs with Linear or Neural Higher-Order Factors -- 6 Conclusion -- References -- Versatile Decision Trees for Learning Over Multiple Contexts -- 1 Introduction -- 2 Dataset Shift -- 3 Versatile Decision Trees -- 3.1 Constructing Splits Using Percentiles -- 3.2 Adapting for Output Shifts -- 3.3 Versatile Model for Decision Trees -- 4 Experimental Results -- 4.1 Generating Synthetic Shifts -- 4.2 Results of the Synthetic Shifts -- 4.3 Results on Non-synthetic Shifts -- 5 Conclusion -- References -- When is Undersampling Effective in Unbalanced Classification Tasks? -- 1 Introduction -- 2 The Warping Effect of Undersampling on the Posterior Probability -- 3 The Interaction Between Warping and Variance of the Estimator -- 4 Experimental Validation -- 4.1 Synthetic Datasets -- 4.2 Real Datasets -- 5 Conclusion -- References -- Clustering and Unsupervised Learning -- A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons -- 1 Introduction -- 2 Related Work -- 3 Kernel Learning with Relative Distances -- 3.1 Basic Definitions -- 3.2 Relative Distance Constraints -- 3.3 Extension to a Kernel Space -- 3.4 Log Determinant Divergence for Kernel Learning -- 3.5 Problem Definition -- 4 Semi-supervised Kernel Learning -- 4.1 Bregman Projections for Constrained Optimization -- 4.2 Semi-supervised Kernel Learning with Relative Comparisons -- Selecting the Bandwidth Parameter. -- Semi-Supervised Kernel Learning with Relative Comparisons. -- Clustering Method. -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Relative Constraints vs. Pairwise Constraints -- 5.3 Multi-resolution Analysis -- 5.4 Generalization Performance -- 5.5 Effect of Equality Constraints -- 6 Conclusion -- References. Bayesian Active Clustering with Pairwise Constraints -- 1 Introduction -- 2 Problem Statement -- 3 Bayesian Active Clustering -- 3.1 The Bayesian Clustering Model -- Marginalization of Cluster Labels. -- 3.2 Active Query Selection -- Selection Criteria. -- Computing the Selection Objectives. -- 3.3 The Sequential MCMC Sampling of W -- 3.4 Find the MAP Solution -- 4 Experiments -- 4.1 Dataset and Setup -- 4.2 Effectiveness of the Proposed Clustering Model -- 4.3 Effectiveness of the Overall Active Clustering Model -- 4.4 Analysis of the Acyclic Graph Restriction -- 5 Related Work -- 6 Conclusion -- References -- ConDist: A Context-Driven Categorical Distance Measure -- 1 Introduction -- 2 Related Work -- 3 The Distance Measure ConDist -- 3.1 Definition of ConDist -- 3.2 Attribute Distance dX -- 3.3 Attribute Weighting Function wX -- 3.4 Correlation, Context and Impact -- 3.5 Heterogeneous Data Sets -- 4 Experiments -- 4.1 Evaluation Methodology -- 4.2 Experiment 1 -- Context Attribute Selection -- 4.3 Experiment 2 -- Comparison in the Context of Classification -- 4.4 Experiment 3 -- Comparison in the Context of Clustering -- 5 Discussion -- 5.1 Experiment 1 -- Context Attribute Selection -- 5.2 Experiment 2 -- Comparison in the Context of Classification -- 5.3 Experiment 3 -- Comparison in the Context of Clustering -- 6 Summary -- References -- Discovering Opinion Spammer Groups by Network Footprints -- 1 Introduction -- 2 Measuring Network Footprints -- 2.1 Neighbor Diversity of Nodes -- 2.2 Self-Similarity in Real-World Graphs -- 2.3 NFS Measure -- 3 Detecting Spammer Groups -- 4 Evaluation -- 4.1 Performance of NFS on Synthetic Data -- 4.2 Performance of GroupStrainer on Synthetic Data -- 4.3 Results on Real-World Data -- 5 Related Work -- 6 Conclusion -- References -- Gamma Process Poisson Factorization for Joint Modeling of Network and Documents. 1 Introduction. |
| Record Nr. | UNISA-996200359403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge
| Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (LVIII, 709 p. 160 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 | 3-319-23528-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Towards Declarative, Domain-OrientedData Analysis -- Sum-Product Networks: Deep Modelswith Tractable Inference -- Mining Online Networks and Communities -- Learning to Acquire Knowledge in a SmartGrid Environment -- Untangling the Web's Invisible Net -- Towards a Digital Time Machine Fueled by BigData and Social Mining -- Abstracts of Journal Track Articles -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Research Track Classification, Regression and Supervised Learning -- Data Split Strategiesfor Evolving Predictive Models -- 1 Introduction -- 2 Data Splits for Model Fitting, Selection,and Assessment -- 3 Issues with Evolving Models -- 4 Data Splits for Evolving Models -- 4.1 Parallel Dump Workflow -- 4.2 Serial Waterfall Workflow -- 4.3 Hybrid Workflow -- 5 Bias Due to Test Set Reuse -- 6 Illustration on Synthetic Data -- 7 Case Study: Paraphrase Detection -- 8 Related Work -- 9 Conclusions -- A Appendix: Bias Due to Test Set Reuse -- References -- Discriminative Interpolation for Classification of Functional Data -- 1 Introduction -- 2 Function Representations and Wavelets -- 3 Related Work -- 4 Classification by Discriminative Interpolation -- 4.1 Training Formulation -- 4.2 Testing Formulation -- 5 Experiments -- 6 Conclusion -- References -- Fast Label Embeddings via Randomized Linear Algebra -- 1 Introduction -- 1.1 Contributions -- 2 Algorithm Derivation -- 2.1 Notation -- 2.2 Background -- 2.3 Rank-Constrained Estimation and Embedding -- 2.4 Rembrandt -- 3 Related Work -- 4 Experiments -- 4.1 ALOI -- 4.2 ODP -- 4.3 LSHTC -- 5 Discussion -- References -- Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation -- 1 Introduction -- 2 General Idea -- 3 Theory -- 4 Closed form Solution for Objective and its Gradient.
5 Experiments -- 6 Conclusions -- References -- Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration -- 1 Introduction -- 2 Proper Scoring Rules -- 2.1 Scoring Rules -- 2.2 Divergence, Entropy and Properness -- 2.3 Expected Loss and Empirical Loss -- 3 Decompositions with Ideal Scores and Calibrated Scores -- 3.1 Ideal Scores Q and the Decomposition L=EL+IL -- 3.2 Calibrated Scores C and the Decomposition L=CL+RL -- 4 Adjusted Scores A and the Decomposition L=AL+PL -- 4.1 Adjustment -- 4.2 The Right Adjustment Procedure Guarantees Decreased Loss -- 5 Decomposition Theorems and Terminology -- 5.1 Decompositions with S,C,Q,Y -- 5.2 Decompositions with S,A,C,Q,Y and Terminology -- 6 Algorithms and Experiments -- 7 Related Work -- 8 Conclusions -- References -- Parameter Learning of Bayesian Network Classifiers Under Computational Constraints -- 1 Introduction -- 2 Related Work -- 3 Background and Notation -- 4 Algorithms for Online Learning of Reduced-Precision Parameters -- 4.1 Learning Maximum Likelihood Parameters -- 4.2 Learning Maximum Margin Parameters -- 5 Experiments -- 5.1 Datasets -- 5.2 Results -- 6 Discussions -- References -- Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning -- 1 Introduction -- 2 Multi-label Classification -- 3 Model Description -- 3.1 Joint Space Embeddings -- 3.2 Learning with Hierarchical Structures Over Labels -- 3.3 Efficient Gradients Computation -- 3.4 Label Ranking to Binary Predictions -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Learning All Labels Together -- 5.2 Learning to Predict Unseen Labels -- 6 Pretrained Label Embeddings as Good Initial Guess -- 6.1 Understanding Label Embeddings -- 6.2 Results -- 7 Conclusions -- Regression with Linear Factored Functions -- 1 Introduction -- 1.1 Kernel Regression. 1.2 Factored Basis Functions -- 2 Regression -- 3 Linear Factored Functions -- 3.1 Function Class -- 3.2 Constraints -- 3.3 Regularization -- 3.4 Optimization -- 4 Empirical Evaluation -- 4.1 Demonstration -- 4.2 Evaluation -- 5 Discussion -- Appendix A LFF Definition and Properties -- Appendix B Inner Loop Derivation -- Appendix C Proofs of the Propositions -- References -- Ridge Regression, Hubness, and Zero-Shot Learning -- 1 Introduction -- 1.1 Background -- 1.2 Research Objective and Contributions -- 2 Zero-Shot Learning as a Regression Problem -- 3 Hubness Phenomenon and the Variance of Data -- 4 Hubness in Regression-Based Zero-Shot Learning -- 4.1 Shrinkage of Projected Objects -- 4.2 Influence of Shrinkage on Nearest Neighbor Search -- 4.3 Additional Argument for Placing Target Objects Closer to the Origin -- 4.4 Summary of the Proposed Approach -- 5 Related Work -- 6 Experiments -- 6.1 Experimental Setups -- 6.2 Task Descriptions and Datasets -- 6.3 Experimental Results -- 7 Conclusion -- References -- Solving Prediction Games with Parallel Batch Gradient Descent -- 1 Introduction -- 2 Problem Setting and Data Transformation Model -- 3 Analysis of Equilibrium Points -- 3.1 Existence of Equilibrium Points -- 3.2 Uniqueness of Equilibrium Points -- 4 Finding the Unique Equilibrium Point Efficiently -- 4.1 Inexact Line Search -- 4.2 Arrow-Hurwicz-Uzawa Method -- 4.3 Parallelized Methods -- 5 Experimental Results -- 5.1 Reference Methods -- 5.2 Performance of the Parameterized Transformation Model -- 5.3 Optimization Algorithms -- 5.4 Parallelized Models -- 6 Conclusion -- References -- Structured Regularizer for Neural Higher-Order Sequence Models -- 1 Introduction -- 2 Related Work -- 3 Higher-Order Conditional Random Fields -- 3.1 Parameter Learning -- 3.2 Forward Algorithm for 2nd-Order CRFs -- 4 Structured Regularizer -- 5 Experiments. 5.1 TIMIT Data Set -- 5.2 Experimental Setup -- 5.3 Labeling Results Using Only MLP Networks -- 5.4 Labeling Results Using LC-CRFs with Linear or Neural Higher-Order Factors -- 6 Conclusion -- References -- Versatile Decision Trees for Learning Over Multiple Contexts -- 1 Introduction -- 2 Dataset Shift -- 3 Versatile Decision Trees -- 3.1 Constructing Splits Using Percentiles -- 3.2 Adapting for Output Shifts -- 3.3 Versatile Model for Decision Trees -- 4 Experimental Results -- 4.1 Generating Synthetic Shifts -- 4.2 Results of the Synthetic Shifts -- 4.3 Results on Non-synthetic Shifts -- 5 Conclusion -- References -- When is Undersampling Effective in Unbalanced Classification Tasks? -- 1 Introduction -- 2 The Warping Effect of Undersampling on the Posterior Probability -- 3 The Interaction Between Warping and Variance of the Estimator -- 4 Experimental Validation -- 4.1 Synthetic Datasets -- 4.2 Real Datasets -- 5 Conclusion -- References -- Clustering and Unsupervised Learning -- A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons -- 1 Introduction -- 2 Related Work -- 3 Kernel Learning with Relative Distances -- 3.1 Basic Definitions -- 3.2 Relative Distance Constraints -- 3.3 Extension to a Kernel Space -- 3.4 Log Determinant Divergence for Kernel Learning -- 3.5 Problem Definition -- 4 Semi-supervised Kernel Learning -- 4.1 Bregman Projections for Constrained Optimization -- 4.2 Semi-supervised Kernel Learning with Relative Comparisons -- Selecting the Bandwidth Parameter. -- Semi-Supervised Kernel Learning with Relative Comparisons. -- Clustering Method. -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Relative Constraints vs. Pairwise Constraints -- 5.3 Multi-resolution Analysis -- 5.4 Generalization Performance -- 5.5 Effect of Equality Constraints -- 6 Conclusion -- References. Bayesian Active Clustering with Pairwise Constraints -- 1 Introduction -- 2 Problem Statement -- 3 Bayesian Active Clustering -- 3.1 The Bayesian Clustering Model -- Marginalization of Cluster Labels. -- 3.2 Active Query Selection -- Selection Criteria. -- Computing the Selection Objectives. -- 3.3 The Sequential MCMC Sampling of W -- 3.4 Find the MAP Solution -- 4 Experiments -- 4.1 Dataset and Setup -- 4.2 Effectiveness of the Proposed Clustering Model -- 4.3 Effectiveness of the Overall Active Clustering Model -- 4.4 Analysis of the Acyclic Graph Restriction -- 5 Related Work -- 6 Conclusion -- References -- ConDist: A Context-Driven Categorical Distance Measure -- 1 Introduction -- 2 Related Work -- 3 The Distance Measure ConDist -- 3.1 Definition of ConDist -- 3.2 Attribute Distance dX -- 3.3 Attribute Weighting Function wX -- 3.4 Correlation, Context and Impact -- 3.5 Heterogeneous Data Sets -- 4 Experiments -- 4.1 Evaluation Methodology -- 4.2 Experiment 1 -- Context Attribute Selection -- 4.3 Experiment 2 -- Comparison in the Context of Classification -- 4.4 Experiment 3 -- Comparison in the Context of Clustering -- 5 Discussion -- 5.1 Experiment 1 -- Context Attribute Selection -- 5.2 Experiment 2 -- Comparison in the Context of Classification -- 5.3 Experiment 3 -- Comparison in the Context of Clustering -- 6 Summary -- References -- Discovering Opinion Spammer Groups by Network Footprints -- 1 Introduction -- 2 Measuring Network Footprints -- 2.1 Neighbor Diversity of Nodes -- 2.2 Self-Similarity in Real-World Graphs -- 2.3 NFS Measure -- 3 Detecting Spammer Groups -- 4 Evaluation -- 4.1 Performance of NFS on Synthetic Data -- 4.2 Performance of GroupStrainer on Synthetic Data -- 4.3 Results on Real-World Data -- 5 Related Work -- 6 Conclusion -- References -- Gamma Process Poisson Factorization for Joint Modeling of Network and Documents. 1 Introduction. |
| Record Nr. | UNINA-9910484000803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
