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Graph Embedding for Pattern Analysis [[electronic resource] /] / edited by Yun Fu, Yunqian Ma
| Graph Embedding for Pattern Analysis [[electronic resource] /] / edited by Yun Fu, Yunqian Ma |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.4 |
| Soggetto topico |
Electrical engineering
Pattern recognition Artificial intelligence Signal processing Image processing Speech processing systems Communications Engineering, Networks Pattern Recognition Artificial Intelligence Signal, Image and Speech Processing |
| ISBN |
1-283-91069-1
1-4614-4457-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces -- Feature Grouping and Selection over an Undirected Graph -- Median Graph Computation by Means of Graph Embedding into Vector Spaces -- Patch Alignment for Graph Embedding -- Feature Subspace Transformations for Enhancing K-Means Clustering -- Learning with ℓ1-Graph for High Dimensional Data Analysis -- Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition -- A Flexible and Effective Linearization Method for Subspace Learning -- A Multi-Graph Spectral Approach for Mining Multi-Source Anomalies -- Graph Embedding for Speaker Recognition. |
| Record Nr. | UNINA-9910437903303321 |
| New York, NY : , : Springer New York : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma
| Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma |
| Pubbl/distr/stampa | Piscataway, NJ : , : IEEE Press |
| Descrizione fisica | 1 online resource (224 p.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Information resources management Information resources - Evaluation System analysis - Mathematical models |
| ISBN |
1-118-64633-9
1-118-64620-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface ix -- Contributors xi -- 1 Introduction 1 -- Haibo He -- 1.1 Problem Formulation, 1 -- 1.2 State-of-the-Art Research, 3 -- 1.3 Looking Ahead: Challenges and Opportunities, 6 -- 1.4 Acknowledgments, 7 -- References, 8 -- 2 Foundations of Imbalanced Learning 13 -- Gary M. Weiss -- 2.1 Introduction, 14 -- 2.2 Background, 14 -- 2.3 Foundational Issues, 19 -- 2.4 Methods for Addressing Imbalanced Data, 26 -- 2.5 Mapping Foundational Issues to Solutions, 35 -- 2.6 Misconceptions About Sampling Methods, 36 -- 2.7 Recommendations and Guidelines, 38 -- References, 38 -- 3 Imbalanced Datasets: From Sampling to Classifiers 43 -- T. Ryan Hoens and Nitesh V. Chawla -- 3.1 Introduction, 43 -- 3.2 Sampling Methods, 44 -- 3.3 Skew-Insensitive Classifiers for Class Imbalance, 49 -- 3.4 Evaluation Metrics, 52 -- 3.5 Discussion, 56 -- References, 57 -- 4 Ensemble Methods for Class Imbalance Learning 61 -- Xu-Ying Liu and Zhi-Hua Zhou -- 4.1 Introduction, 61 -- 4.2 Ensemble Methods, 62 -- 4.3 Ensemble Methods for Class Imbalance Learning, 66 -- 4.4 Empirical Study, 73 -- 4.5 Concluding Remarks, 79 -- References, 80 -- 5 Class Imbalance Learning Methods for Support Vector Machines 83 -- Rukshan Batuwita and Vasile Palade -- 5.1 Introduction, 83 -- 5.2 Introduction to Support Vector Machines, 84 -- 5.3 SVMs and Class Imbalance, 86 -- 5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods, 87 -- 5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 88 -- 5.6 Summary, 96 -- References, 96 -- 6 Class Imbalance and Active Learning 101 -- Josh Attenberg and Sd eyda Ertekin -- 6.1 Introduction, 102 -- 6.2 Active Learning for Imbalanced Problems, 103 -- 6.3 Active Learning for Imbalanced Data Classification, 110 -- 6.4 Adaptive Resampling with Active Learning, 122 -- 6.5 Difficulties with Extreme Class Imbalance, 129 -- 6.6 Dealing with Disjunctive Classes, 130 -- 6.7 Starting Cold, 132 -- 6.8 Alternatives to Active Learning for Imbalanced Problems, 133.
6.9 Conclusion, 144 -- References, 145 -- 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151 -- Sheng Chen and Haibo He -- 7.1 Introduction, 152 -- 7.2 Preliminaries, 154 -- 7.3 Algorithms, 157 -- 7.4 Simulation, 167 -- 7.5 Conclusion, 182 -- 7.6 Acknowledgments, 183 -- References, 184 -- 8 Assessment Metrics for Imbalanced Learning 187 -- Nathalie Japkowicz -- 8.1 Introduction, 187 -- 8.2 A Review of Evaluation Metric Families and their Applicability -- to the Class Imbalance Problem, 189 -- 8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190 -- 8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196 -- 8.5 Conclusion, 204 -- 8.6 Acknowledgments, 205 -- References, 205 -- Index 207. |
| Record Nr. | UNINA-9910141572403321 |
| Piscataway, NJ : , : IEEE Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma
| Imbalanced learning : foundations, algorithms, and applications / / edited by Haibo He, Yunqian Ma |
| Pubbl/distr/stampa | Piscataway, NJ : , : IEEE Press |
| Descrizione fisica | 1 online resource (224 p.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Information resources management Information resources - Evaluation System analysis - Mathematical models |
| ISBN |
1-118-64633-9
1-118-64620-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface ix -- Contributors xi -- 1 Introduction 1 -- Haibo He -- 1.1 Problem Formulation, 1 -- 1.2 State-of-the-Art Research, 3 -- 1.3 Looking Ahead: Challenges and Opportunities, 6 -- 1.4 Acknowledgments, 7 -- References, 8 -- 2 Foundations of Imbalanced Learning 13 -- Gary M. Weiss -- 2.1 Introduction, 14 -- 2.2 Background, 14 -- 2.3 Foundational Issues, 19 -- 2.4 Methods for Addressing Imbalanced Data, 26 -- 2.5 Mapping Foundational Issues to Solutions, 35 -- 2.6 Misconceptions About Sampling Methods, 36 -- 2.7 Recommendations and Guidelines, 38 -- References, 38 -- 3 Imbalanced Datasets: From Sampling to Classifiers 43 -- T. Ryan Hoens and Nitesh V. Chawla -- 3.1 Introduction, 43 -- 3.2 Sampling Methods, 44 -- 3.3 Skew-Insensitive Classifiers for Class Imbalance, 49 -- 3.4 Evaluation Metrics, 52 -- 3.5 Discussion, 56 -- References, 57 -- 4 Ensemble Methods for Class Imbalance Learning 61 -- Xu-Ying Liu and Zhi-Hua Zhou -- 4.1 Introduction, 61 -- 4.2 Ensemble Methods, 62 -- 4.3 Ensemble Methods for Class Imbalance Learning, 66 -- 4.4 Empirical Study, 73 -- 4.5 Concluding Remarks, 79 -- References, 80 -- 5 Class Imbalance Learning Methods for Support Vector Machines 83 -- Rukshan Batuwita and Vasile Palade -- 5.1 Introduction, 83 -- 5.2 Introduction to Support Vector Machines, 84 -- 5.3 SVMs and Class Imbalance, 86 -- 5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods, 87 -- 5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 88 -- 5.6 Summary, 96 -- References, 96 -- 6 Class Imbalance and Active Learning 101 -- Josh Attenberg and Sd eyda Ertekin -- 6.1 Introduction, 102 -- 6.2 Active Learning for Imbalanced Problems, 103 -- 6.3 Active Learning for Imbalanced Data Classification, 110 -- 6.4 Adaptive Resampling with Active Learning, 122 -- 6.5 Difficulties with Extreme Class Imbalance, 129 -- 6.6 Dealing with Disjunctive Classes, 130 -- 6.7 Starting Cold, 132 -- 6.8 Alternatives to Active Learning for Imbalanced Problems, 133.
6.9 Conclusion, 144 -- References, 145 -- 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151 -- Sheng Chen and Haibo He -- 7.1 Introduction, 152 -- 7.2 Preliminaries, 154 -- 7.3 Algorithms, 157 -- 7.4 Simulation, 167 -- 7.5 Conclusion, 182 -- 7.6 Acknowledgments, 183 -- References, 184 -- 8 Assessment Metrics for Imbalanced Learning 187 -- Nathalie Japkowicz -- 8.1 Introduction, 187 -- 8.2 A Review of Evaluation Metric Families and their Applicability -- to the Class Imbalance Problem, 189 -- 8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190 -- 8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196 -- 8.5 Conclusion, 204 -- 8.6 Acknowledgments, 205 -- References, 205 -- Index 207. |
| Record Nr. | UNINA-9910830339103321 |
| Piscataway, NJ : , : IEEE Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Support Vector Machines Applications / / edited by Yunqian Ma, Guodong Guo
| Support Vector Machines Applications / / edited by Yunqian Ma, Guodong Guo |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina |
004.6
006.3 006.31 620 |
| Soggetto topico |
Signal processing
Image processing Speech processing systems Computer communication systems Computational complexity Computational intelligence Computer organization Electrical engineering Signal, Image and Speech Processing Computer Communication Networks Complexity Computational Intelligence Computer Systems Organization and Communication Networks Communications Engineering, Networks |
| ISBN | 3-319-02300-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Augmented-SVM for gradient observations with application to learning multiple-attractor dynamics -- Multi-class Support Vector Machine -- Novel Inductive and Transductive Transfer Learning Approaches Based on Support Vector Learning -- Security Evaluation of Support Vector Machines in Adversarial Environments -- Application of SVMs to the Bag-of-features Model— A Kernel Perspective -- Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination -- Kernel Machines for Imbalanced Data Problem and the Use in Biomedical Applications -- Soft Biometrics from Face Images using Support Vector Machines. |
| Record Nr. | UNINA-9910299491503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||