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Algorithmic decision theory : 7th international conference, ADT 2021, Toulouse, France, November 3-5, 2021 : proceedings / / edited by Dimitris Fotakis, David Ríos Insua
| Algorithmic decision theory : 7th international conference, ADT 2021, Toulouse, France, November 3-5, 2021 : proceedings / / edited by Dimitris Fotakis, David Ríos Insua |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (446 pages) |
| Disciplina | 519.542 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Decision trees
Data mining Decision making - Mathematical models |
| ISBN | 3-030-87756-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464502703316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Binary decision diagrams and extensions for system reliability analysis / / Liudong Xing, Suprasad V. Amari ; cover design by Russell Richardson
| Binary decision diagrams and extensions for system reliability analysis / / Liudong Xing, Suprasad V. Amari ; cover design by Russell Richardson |
| Autore | Xing Liudong |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (393 p.) |
| Disciplina | 620/.00452 |
| Collana | Performability Engineering Series |
| Soggetto topico |
Reliability (Engineering) - Graphic methods
System analysis - Graphic methods Decision trees |
| ISBN |
1-119-17800-2
1-119-17802-9 1-119-17801-0 |
| Classificazione | TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Preface xiii Nomenclature xix 1 Introduction 1 1.1 Historical Developments 1 1.2 Reliability and Safety Applications 4 2 Basic Reliability Theory and Models 7 2.1 Probabiltiy Concepts 7 2.2 Reliability Measures 14 2.3 Fault Tree Analysis 17 3 Fundamentals of Binary Decision Diagrams 33 3.1 Preliminaries 34 3.2 Basic Concepts 34 3.3 BDD Construction 35 3.4 BDD Evaluation 42 3.5 BDD-Based Software Package 44 4 Application of BDD to Binary-State Systems 45 4.1 Network Reliability Analysis 45 4.2 Event Tree Analysis 47 4.3 Failure Frequency Analysis 50 4.4 Importance Measures and Analysis 54 4.5 Modularization Methods 60 4.6 Non-Coherent Systems 60 4.7 Disjoint Failures 65 4.8 Dependent Failures 68 5 Phased-Mission Systems 73 5.1 System Description 74 5.2 Rules of Phase Algebra 75 5.3 BDD-Based Method for PMS Analysis 76 5.4 Mission Performance Analysis 81 6 Multi-State Systems 85 6.1 Assumptions 86 6.2 An Illustrative Example 86 6.3 MSS Representation 87 6.4 Multi-State BDD (MBDD) 90 6.5 Logarithmically-Encoded BDD (LBDD) 94 6.6 Multi-State Multi-Valued Decision Diagrams (MMDD) 98 6.7 Performance Evaluation and Benchmarks 102 6.8 Summary 117 7 Fault Tolerant Systems and Coverage Models 119 7.1 Basic Types 120 7.2 Imperfect Coverage Model 122 7.3 Applications to Binary-State Systems 123 7.4 Applications to Multi-State Systems 129 7.5 Applications to Phased-Mission Systems 133 7.6 Summary 139 8 Shared Decision Diagrams 143 8.1 Multi-Rooted Decision Diagrams 144 8.2 Multi-Terminal Decision Diagrams 148 8.3 Performance Study on Multi-State Systems 151 8.4 Application to Phased-Mission Systems 163 8.5 Application to Multi-State k-out-of-n Systems 168 8.6 Importance Measures 176 8.7 Failure Frequency Based Measures 180 8.8 Summary 183 Conclusions 185 References 187 Index 205 . |
| Record Nr. | UNINA-9910131431103321 |
Xing Liudong
|
||
| Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Binary decision diagrams and extensions for system reliability analysis / / Liudong Xing, Suprasad V. Amari ; cover design by Russell Richardson
| Binary decision diagrams and extensions for system reliability analysis / / Liudong Xing, Suprasad V. Amari ; cover design by Russell Richardson |
| Autore | Xing Liudong |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (393 p.) |
| Disciplina | 620/.00452 |
| Collana | Performability Engineering Series |
| Soggetto topico |
Reliability (Engineering) - Graphic methods
System analysis - Graphic methods Decision trees |
| ISBN |
1-119-17800-2
1-119-17802-9 1-119-17801-0 |
| Classificazione | TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Preface xiii Nomenclature xix 1 Introduction 1 1.1 Historical Developments 1 1.2 Reliability and Safety Applications 4 2 Basic Reliability Theory and Models 7 2.1 Probabiltiy Concepts 7 2.2 Reliability Measures 14 2.3 Fault Tree Analysis 17 3 Fundamentals of Binary Decision Diagrams 33 3.1 Preliminaries 34 3.2 Basic Concepts 34 3.3 BDD Construction 35 3.4 BDD Evaluation 42 3.5 BDD-Based Software Package 44 4 Application of BDD to Binary-State Systems 45 4.1 Network Reliability Analysis 45 4.2 Event Tree Analysis 47 4.3 Failure Frequency Analysis 50 4.4 Importance Measures and Analysis 54 4.5 Modularization Methods 60 4.6 Non-Coherent Systems 60 4.7 Disjoint Failures 65 4.8 Dependent Failures 68 5 Phased-Mission Systems 73 5.1 System Description 74 5.2 Rules of Phase Algebra 75 5.3 BDD-Based Method for PMS Analysis 76 5.4 Mission Performance Analysis 81 6 Multi-State Systems 85 6.1 Assumptions 86 6.2 An Illustrative Example 86 6.3 MSS Representation 87 6.4 Multi-State BDD (MBDD) 90 6.5 Logarithmically-Encoded BDD (LBDD) 94 6.6 Multi-State Multi-Valued Decision Diagrams (MMDD) 98 6.7 Performance Evaluation and Benchmarks 102 6.8 Summary 117 7 Fault Tolerant Systems and Coverage Models 119 7.1 Basic Types 120 7.2 Imperfect Coverage Model 122 7.3 Applications to Binary-State Systems 123 7.4 Applications to Multi-State Systems 129 7.5 Applications to Phased-Mission Systems 133 7.6 Summary 139 8 Shared Decision Diagrams 143 8.1 Multi-Rooted Decision Diagrams 144 8.2 Multi-Terminal Decision Diagrams 148 8.3 Performance Study on Multi-State Systems 151 8.4 Application to Phased-Mission Systems 163 8.5 Application to Multi-State k-out-of-n Systems 168 8.6 Importance Measures 176 8.7 Failure Frequency Based Measures 180 8.8 Summary 183 Conclusions 185 References 187 Index 205 . |
| Record Nr. | UNINA-9910811661403321 |
|
Xing Liudong
|
||
| Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon
| Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon |
| Autore | Rokach Lior |
| Pubbl/distr/stampa | Singapore, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) | MaimonOded Z |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Data mining
Decision trees |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-91179-8
9786611911799 981-277-172-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Contents; 1. Introduction to Decision Trees; 1.1 Data Mining and Knowledge Discovery; 1.2 Taxonomy of Data Mining Methods; 1.3 Supervised Methods; 1.3.1 Overview; 1.4 Classification Trees; 1.5 Characteristics of Classification Trees; 1.5.1 Tree Size; 1.5.2 The hierarchical nature of decision trees; 1.6 Relation to Rule Induction; 2. Growing Decision Trees; 2.0.1 Training Set; 2.0.2 Definition of the Classification Problem; 2.0.3 Induction Algorithms; 2.0.4 Probability Estimation in Decision Trees; 2.0.4.1 Laplace Correction; 2.0.4.2 No Match
2.1 Algorithmic Framework for Decision Trees2.2 Stopping Criteria; 3. Evaluation of Classification Trees; 3.1 Overview; 3.2 Generalization Error; 3.2.1 Theoretical Estimation of Generalization Error; 3.2.2 Empirical Estimation of Generalization Error; 3.2.3 Alternatives to the Accuracy Measure; 3.2.4 The F-Measure; 3.2.5 Confusion Matrix; 3.2.6 Classifier Evaluation under Limited Resources; 3.2.6.1 ROC Curves; 3.2.6.2 Hit Rate Curve; 3.2.6.3 Qrecall (Quota Recall); 3.2.6.4 Lift Curve; 3.2.6.5 Pearson Correlation Coegfficient; 3.2.6.6 Area Under Curve (AUC); 3.2.6.7 Average Hit Rate 3.2.6.8 Average Qrecall3.2.6.9 Potential Extract Measure (PEM); 3.2.7 Which Decision Tree Classifier is Better?; 3.2.7.1 McNemar's Test; 3.2.7.2 A Test for the Difference of Two Proportions; 3.2.7.3 The Resampled Paired t Test; 3.2.7.4 The k-fold Cross-validated Paired t Test; 3.3 Computational Complexity; 3.4 Comprehensibility; 3.5 Scalability to Large Datasets; 3.6 Robustness; 3.7 Stability; 3.8 Interestingness Measures; 3.9 Overfitting and Underfitting; 3.10 "No Free Lunch" Theorem; 4. Splitting Criteria; 4.1 Univariate Splitting Criteria; 4.1.1 Overview; 4.1.2 Impurity based Criteria 4.1.3 Information Gain4.1.4 Gini Index; 4.1.5 Likelihood Ratio Chi-squared Statistics; 4.1.6 DKM Criterion; 4.1.7 Normalized Impurity-based Criteria; 4.1.8 Gain Ratio; 4.1.9 Distance Measure; 4.1.10 Binary Criteria; 4.1.11 Twoing Criterion; 4.1.12 Orthogonal Criterion; 4.1.13 Kolmogorov-Smirnov Criterion; 4.1.14 AUC Splitting Criteria; 4.1.15 Other Univariate Splitting Criteria; 4.1.16 Comparison of Univariate Splitting Criteria; 4.2 Handling Missing Values; 5. Pruning Trees; 5.1 Stopping Criteria; 5.2 Heuristic Pruning; 5.2.1 Overview; 5.2.2 Cost Complexity Pruning 5.2.3 Reduced Error Pruning5.2.4 Minimum Error Pruning (MEP); 5.2.5 Pessimistic Pruning; 5.2.6 Error-Based Pruning (EBP); 5.2.7 Minimum Description Length (MDL) Pruning; 5.2.8 Other Pruning Methods; 5.2.9 Comparison of Pruning Methods; 5.3 Optimal Pruning; 6. Advanced Decision Trees; 6.1 Survey of Common Algorithms for Decision Tree Induction; 6.1.1 ID3; 6.1.2 C4.5; 6.1.3 CART; 6.1.4 CHAID; 6.1.5 QUEST.; 6.1.6 Reference to Other Algorithms; 6.1.7 Advantages and Disadvantages of Decision Trees; 6.1.8 Oblivious Decision Trees; 6.1.9 Decision Trees Inducers for Large Datasets 6.1.10 Online Adaptive Decision Trees |
| Record Nr. | UNINA-9910450810803321 |
|
Rokach Lior
|
||
| Singapore, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon
| Data mining with decision trees [[electronic resource] /] : theory and applications / / Lior Rokach, Oded Maimon |
| Autore | Rokach Lior |
| Pubbl/distr/stampa | Singapore, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) | MaimonOded Z |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Data mining
Decision trees |
| ISBN |
1-281-91179-8
9786611911799 981-277-172-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Contents; 1. Introduction to Decision Trees; 1.1 Data Mining and Knowledge Discovery; 1.2 Taxonomy of Data Mining Methods; 1.3 Supervised Methods; 1.3.1 Overview; 1.4 Classification Trees; 1.5 Characteristics of Classification Trees; 1.5.1 Tree Size; 1.5.2 The hierarchical nature of decision trees; 1.6 Relation to Rule Induction; 2. Growing Decision Trees; 2.0.1 Training Set; 2.0.2 Definition of the Classification Problem; 2.0.3 Induction Algorithms; 2.0.4 Probability Estimation in Decision Trees; 2.0.4.1 Laplace Correction; 2.0.4.2 No Match
2.1 Algorithmic Framework for Decision Trees2.2 Stopping Criteria; 3. Evaluation of Classification Trees; 3.1 Overview; 3.2 Generalization Error; 3.2.1 Theoretical Estimation of Generalization Error; 3.2.2 Empirical Estimation of Generalization Error; 3.2.3 Alternatives to the Accuracy Measure; 3.2.4 The F-Measure; 3.2.5 Confusion Matrix; 3.2.6 Classifier Evaluation under Limited Resources; 3.2.6.1 ROC Curves; 3.2.6.2 Hit Rate Curve; 3.2.6.3 Qrecall (Quota Recall); 3.2.6.4 Lift Curve; 3.2.6.5 Pearson Correlation Coegfficient; 3.2.6.6 Area Under Curve (AUC); 3.2.6.7 Average Hit Rate 3.2.6.8 Average Qrecall3.2.6.9 Potential Extract Measure (PEM); 3.2.7 Which Decision Tree Classifier is Better?; 3.2.7.1 McNemar's Test; 3.2.7.2 A Test for the Difference of Two Proportions; 3.2.7.3 The Resampled Paired t Test; 3.2.7.4 The k-fold Cross-validated Paired t Test; 3.3 Computational Complexity; 3.4 Comprehensibility; 3.5 Scalability to Large Datasets; 3.6 Robustness; 3.7 Stability; 3.8 Interestingness Measures; 3.9 Overfitting and Underfitting; 3.10 "No Free Lunch" Theorem; 4. Splitting Criteria; 4.1 Univariate Splitting Criteria; 4.1.1 Overview; 4.1.2 Impurity based Criteria 4.1.3 Information Gain4.1.4 Gini Index; 4.1.5 Likelihood Ratio Chi-squared Statistics; 4.1.6 DKM Criterion; 4.1.7 Normalized Impurity-based Criteria; 4.1.8 Gain Ratio; 4.1.9 Distance Measure; 4.1.10 Binary Criteria; 4.1.11 Twoing Criterion; 4.1.12 Orthogonal Criterion; 4.1.13 Kolmogorov-Smirnov Criterion; 4.1.14 AUC Splitting Criteria; 4.1.15 Other Univariate Splitting Criteria; 4.1.16 Comparison of Univariate Splitting Criteria; 4.2 Handling Missing Values; 5. Pruning Trees; 5.1 Stopping Criteria; 5.2 Heuristic Pruning; 5.2.1 Overview; 5.2.2 Cost Complexity Pruning 5.2.3 Reduced Error Pruning5.2.4 Minimum Error Pruning (MEP); 5.2.5 Pessimistic Pruning; 5.2.6 Error-Based Pruning (EBP); 5.2.7 Minimum Description Length (MDL) Pruning; 5.2.8 Other Pruning Methods; 5.2.9 Comparison of Pruning Methods; 5.3 Optimal Pruning; 6. Advanced Decision Trees; 6.1 Survey of Common Algorithms for Decision Tree Induction; 6.1.1 ID3; 6.1.2 C4.5; 6.1.3 CART; 6.1.4 CHAID; 6.1.5 QUEST.; 6.1.6 Reference to Other Algorithms; 6.1.7 Advantages and Disadvantages of Decision Trees; 6.1.8 Oblivious Decision Trees; 6.1.9 Decision Trees Inducers for Large Datasets 6.1.10 Online Adaptive Decision Trees |
| Record Nr. | UNINA-9910784996003321 |
|
Rokach Lior
|
||
| Singapore, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining with decision trees : theroy and applications / / Lior Rokach, Oded Maimon
| Data mining with decision trees : theroy and applications / / Lior Rokach, Oded Maimon |
| Autore | Rokach Lior |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) | MaimonOded |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Data mining
Decision trees |
| ISBN |
9786611911799
9781281911797 1281911798 9789812771728 9812771727 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Contents; 1. Introduction to Decision Trees; 1.1 Data Mining and Knowledge Discovery; 1.2 Taxonomy of Data Mining Methods; 1.3 Supervised Methods; 1.3.1 Overview; 1.4 Classification Trees; 1.5 Characteristics of Classification Trees; 1.5.1 Tree Size; 1.5.2 The hierarchical nature of decision trees; 1.6 Relation to Rule Induction; 2. Growing Decision Trees; 2.0.1 Training Set; 2.0.2 Definition of the Classification Problem; 2.0.3 Induction Algorithms; 2.0.4 Probability Estimation in Decision Trees; 2.0.4.1 Laplace Correction; 2.0.4.2 No Match
2.1 Algorithmic Framework for Decision Trees2.2 Stopping Criteria; 3. Evaluation of Classification Trees; 3.1 Overview; 3.2 Generalization Error; 3.2.1 Theoretical Estimation of Generalization Error; 3.2.2 Empirical Estimation of Generalization Error; 3.2.3 Alternatives to the Accuracy Measure; 3.2.4 The F-Measure; 3.2.5 Confusion Matrix; 3.2.6 Classifier Evaluation under Limited Resources; 3.2.6.1 ROC Curves; 3.2.6.2 Hit Rate Curve; 3.2.6.3 Qrecall (Quota Recall); 3.2.6.4 Lift Curve; 3.2.6.5 Pearson Correlation Coegfficient; 3.2.6.6 Area Under Curve (AUC); 3.2.6.7 Average Hit Rate 3.2.6.8 Average Qrecall3.2.6.9 Potential Extract Measure (PEM); 3.2.7 Which Decision Tree Classifier is Better?; 3.2.7.1 McNemar's Test; 3.2.7.2 A Test for the Difference of Two Proportions; 3.2.7.3 The Resampled Paired t Test; 3.2.7.4 The k-fold Cross-validated Paired t Test; 3.3 Computational Complexity; 3.4 Comprehensibility; 3.5 Scalability to Large Datasets; 3.6 Robustness; 3.7 Stability; 3.8 Interestingness Measures; 3.9 Overfitting and Underfitting; 3.10 "No Free Lunch" Theorem; 4. Splitting Criteria; 4.1 Univariate Splitting Criteria; 4.1.1 Overview; 4.1.2 Impurity based Criteria 4.1.3 Information Gain4.1.4 Gini Index; 4.1.5 Likelihood Ratio Chi-squared Statistics; 4.1.6 DKM Criterion; 4.1.7 Normalized Impurity-based Criteria; 4.1.8 Gain Ratio; 4.1.9 Distance Measure; 4.1.10 Binary Criteria; 4.1.11 Twoing Criterion; 4.1.12 Orthogonal Criterion; 4.1.13 Kolmogorov-Smirnov Criterion; 4.1.14 AUC Splitting Criteria; 4.1.15 Other Univariate Splitting Criteria; 4.1.16 Comparison of Univariate Splitting Criteria; 4.2 Handling Missing Values; 5. Pruning Trees; 5.1 Stopping Criteria; 5.2 Heuristic Pruning; 5.2.1 Overview; 5.2.2 Cost Complexity Pruning 5.2.3 Reduced Error Pruning5.2.4 Minimum Error Pruning (MEP); 5.2.5 Pessimistic Pruning; 5.2.6 Error-Based Pruning (EBP); 5.2.7 Minimum Description Length (MDL) Pruning; 5.2.8 Other Pruning Methods; 5.2.9 Comparison of Pruning Methods; 5.3 Optimal Pruning; 6. Advanced Decision Trees; 6.1 Survey of Common Algorithms for Decision Tree Induction; 6.1.1 ID3; 6.1.2 C4.5; 6.1.3 CART; 6.1.4 CHAID; 6.1.5 QUEST.; 6.1.6 Reference to Other Algorithms; 6.1.7 Advantages and Disadvantages of Decision Trees; 6.1.8 Oblivious Decision Trees; 6.1.9 Decision Trees Inducers for Large Datasets 6.1.10 Online Adaptive Decision Trees |
| Record Nr. | UNINA-9910974375803321 |
|
Rokach Lior
|
||
| Singapore, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Decision forests for computer vision and medical image analysis / / A. Criminisi, J. Shotton, editors
| Decision forests for computer vision and medical image analysis / / A. Criminisi, J. Shotton, editors |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | London ; ; New York, : Springer, c2013 |
| Descrizione fisica | 1 online resource (xix, 368 pages) : illustrations (some color) |
| Disciplina | 511.52 |
| Altri autori (Persone) |
CriminisiAntonio <1972->
ShottonJ |
| Collana | Advances in computer vision and pattern recognition |
| Soggetto topico |
Decision trees
Computer vision Image processing - Digital techniques Diagnostic imaging - Digital techniques |
| ISBN |
1-299-33614-0
1-4471-4929-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview and Scope -- Notation and Terminology -- Part I: The Decision Forest Model -- Introduction -- Classification Forests -- Regression Forests -- Density Forests -- Manifold Forests -- Semi-Supervised Classification Forests -- Part II: Applications in Computer Vision and Medical Image Analysis -- Keypoint Recognition Using Random Forests and Random Ferns -- Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval -- Class-Specific Hough Forests for Object Detection -- Hough-Based Tracking of Deformable Objects -- Efficient Human Pose Estimation from Single Depth Images -- Anatomy Detection and Localization in 3D Medical Images -- Semantic Texton Forests for Image Categorization and Segmentation -- Semi-Supervised Video Segmentation Using Decision Forests -- Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI -- Manifold Forests for Multi-Modality Classification of Alzheimer’s Disease -- Entangled Forests and Differentiable Information Gain Maximization -- Decision Tree Fields -- Part III: Implementation and Conclusion -- Efficient Implementation of Decision Forests -- The Sherwood Software Library -- Conclusions. |
| Record Nr. | UNINA-9910437568003321 |
| London ; ; New York, : Springer, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning with uncertainty / / Xizhao Wang, Junhai Zhai
| Learning with uncertainty / / Xizhao Wang, Junhai Zhai |
| Autore | Wang Xizhao |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , [2017] |
| Descrizione fisica | 1 online resource (240 pages) : illustrations, tables |
| Disciplina | 006.3/1 |
| Soggetto topico |
Machine learning
Fuzzy decision making Decision trees |
| ISBN |
1-315-37069-7
1-4987-2413-2 1-315-35356-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Uncertainty -- 2. Decision tree with uncertainty -- 3. Clustering under uncertainty environment -- 4. Active learning with uncertainty -- 5. Ensemble learning with uncertainty. |
| Record Nr. | UNINA-9910153185403321 |
|
Wang Xizhao
|
||
| Boca Raton : , : CRC Press, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Simplicial complexes of graphs / / Jakob Jonsson
| Simplicial complexes of graphs / / Jakob Jonsson |
| Autore | Jonsson Jakob <1972-> |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (XIV, 382 p. 34 illus.) |
| Disciplina | 511.5 |
| Collana | Lecture Notes in Mathematics |
| Soggetto topico |
Decision trees
Graph theory Morse theory Algebra, Homological |
| ISBN | 3-540-75859-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | and Basic Concepts -- and Overview -- Abstract Graphs and Set Systems -- Simplicial Topology -- Tools -- Discrete Morse Theory -- Decision Trees -- Miscellaneous Results -- Overview of Graph Complexes -- Graph Properties -- Dihedral Graph Properties -- Digraph Properties -- Main Goals and Proof Techniques -- Vertex Degree -- Matchings -- Graphs of Bounded Degree -- Cycles and Crossings -- Forests and Matroids -- Bipartite Graphs -- Directed Variants of Forests and Bipartite Graphs -- Noncrossing Graphs -- Non-Hamiltonian Graphs -- Connectivity -- Disconnected Graphs -- Not 2-connected Graphs -- Not 3-connected Graphs and Beyond -- Dihedral Variants of k-connected Graphs -- Directed Variants of Connected Graphs -- Not 2-edge-connected Graphs -- Cliques and Stable Sets -- Graphs Avoiding k-matchings -- t-colorable Graphs -- Graphs and Hypergraphs with Bounded Covering Number -- Open Problems -- Open Problems. |
| Record Nr. | UNISA-996466508603316 |
|
Jonsson Jakob <1972->
|
||
| Berlin, Germany : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Simplicial complexes of graphs / / Jakob Jonsson
| Simplicial complexes of graphs / / Jakob Jonsson |
| Autore | Jonsson Jakob <1972-> |
| Edizione | [1st ed. 2008.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer, , [2008] |
| Descrizione fisica | 1 online resource (XIV, 382 p. 34 illus.) |
| Disciplina | 511.5 |
| Collana | Lecture Notes in Mathematics |
| Soggetto topico |
Decision trees
Graph theory Morse theory Algebra, Homological |
| ISBN | 3-540-75859-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | and Basic Concepts -- and Overview -- Abstract Graphs and Set Systems -- Simplicial Topology -- Tools -- Discrete Morse Theory -- Decision Trees -- Miscellaneous Results -- Overview of Graph Complexes -- Graph Properties -- Dihedral Graph Properties -- Digraph Properties -- Main Goals and Proof Techniques -- Vertex Degree -- Matchings -- Graphs of Bounded Degree -- Cycles and Crossings -- Forests and Matroids -- Bipartite Graphs -- Directed Variants of Forests and Bipartite Graphs -- Noncrossing Graphs -- Non-Hamiltonian Graphs -- Connectivity -- Disconnected Graphs -- Not 2-connected Graphs -- Not 3-connected Graphs and Beyond -- Dihedral Variants of k-connected Graphs -- Directed Variants of Connected Graphs -- Not 2-edge-connected Graphs -- Cliques and Stable Sets -- Graphs Avoiding k-matchings -- t-colorable Graphs -- Graphs and Hypergraphs with Bounded Covering Number -- Open Problems -- Open Problems. |
| Record Nr. | UNINA-9910484516703321 |
|
Jonsson Jakob <1972->
|
||
| Berlin, Germany : , : Springer, , [2008] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Soggetto topico
-
Decision trees
(11)
- Data mining (4)
- Algebra, Homological (2)
- Graph theory (2)
- Morse theory (2)