Pattern classification using ensemble methods:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific Pub. Co.
c2010
|
| Schriftenreihe: | Series in machine perception and artificial intelligence
v. 75 |
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references (p. 185-222) and index 1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms. 1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction methods -- 2. Introduction to ensemble learning. 2.1. Back to the roots. 2.2. The wisdom of crowds. 2.3. The bagging algorithm. 2.4. The boosting algorithm. 2.5. The AdaBoost algorithm. 2.6. No free lunch theorem and ensemble learning. 2.7. Bias-variance decomposition and ensemble learning. 2.8. Occam's razor and ensemble learning. 2.9. Classifier dependency. 2.10. Ensemble methods for advanced classification tasks -- 3. Ensemble classification. 3.1. Fusions methods. 3.2. Selecting classification. 3.3. Mixture of experts and meta learning -- 4. Ensemble diversity. 4.1. Overview. 4.2. Manipulating the inducer. 4.3. Manipulating the training samples. 4.4. Manipulating the target attribute representation. 4.5. Partitioning the search space. 4.6. Multi-inducers. 4.7. Measuring the diversity -- 5. Ensemble selection. 5.1. Ensemble selection. 5.2. Pre selection of the ensemble size. 5.3. Selection of the ensemble size while training. 5.4. Pruning -- post selection of the ensemble size -- 6. Error correcting output codes. 6.1. Code-matrix decomposition of multiclass problems. 6.2. Type I -- training an ensemble given a code-matrix. 6.3. Type II -- adapting code-matrices to the multiclass problems -- 7. Evaluating ensembles of classifiers. 7.1. Generalization error. 7.2. Computational complexity. 7.3. Interpretability of the resulting ensemble. 7.4. Scalability to large datasets. 7.5. Robustness. 7.6. Stability. 7.7. Flexibility. 7.8. Usability. 7.9. Software availability. 7.10. Which ensemble method should be used? Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method |
| Beschreibung: | 1 Online-Ressource (xv, 225 p.) |
| ISBN: | 9789814271066 9789814271073 9814271063 9814271071 |
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| 500 | |a Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method | ||
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| 650 | 4 | |a Théorie des ensembles | |
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Datensatz im Suchindex
| _version_ | 1804175506734579712 |
|---|---|
| any_adam_object | |
| author | Rokach, Lior |
| author_facet | Rokach, Lior |
| author_role | aut |
| author_sort | Rokach, Lior |
| author_variant | l r lr |
| building | Verbundindex |
| bvnumber | BV043098870 |
| collection | ZDB-4-EBA |
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| dewey-full | 006.4 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.4 |
| dewey-search | 006.4 |
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| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV043098870 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:17:23Z |
| institution | BVB |
| isbn | 9789814271066 9789814271073 9814271063 9814271071 |
| language | English |
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| owner_facet | DE-1046 DE-1047 |
| physical | 1 Online-Ressource (xv, 225 p.) |
| psigel | ZDB-4-EBA ZDB-4-EBA FAW_PDA_EBA |
| publishDate | 2010 |
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| publisher | World Scientific Pub. Co. |
| record_format | marc |
| series2 | Series in machine perception and artificial intelligence |
| spelling | Rokach, Lior Verfasser aut Pattern classification using ensemble methods Lior Rokach Singapore World Scientific Pub. Co. c2010 1 Online-Ressource (xv, 225 p.) txt rdacontent c rdamedia cr rdacarrier Series in machine perception and artificial intelligence v. 75 Includes bibliographical references (p. 185-222) and index 1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms. 1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction methods -- 2. Introduction to ensemble learning. 2.1. Back to the roots. 2.2. The wisdom of crowds. 2.3. The bagging algorithm. 2.4. The boosting algorithm. 2.5. The AdaBoost algorithm. 2.6. No free lunch theorem and ensemble learning. 2.7. Bias-variance decomposition and ensemble learning. 2.8. Occam's razor and ensemble learning. 2.9. Classifier dependency. 2.10. Ensemble methods for advanced classification tasks -- 3. Ensemble classification. 3.1. Fusions methods. 3.2. Selecting classification. 3.3. Mixture of experts and meta learning -- 4. Ensemble diversity. 4.1. Overview. 4.2. Manipulating the inducer. 4.3. Manipulating the training samples. 4.4. Manipulating the target attribute representation. 4.5. Partitioning the search space. 4.6. Multi-inducers. 4.7. Measuring the diversity -- 5. Ensemble selection. 5.1. Ensemble selection. 5.2. Pre selection of the ensemble size. 5.3. Selection of the ensemble size while training. 5.4. Pruning -- post selection of the ensemble size -- 6. Error correcting output codes. 6.1. Code-matrix decomposition of multiclass problems. 6.2. Type I -- training an ensemble given a code-matrix. 6.3. Type II -- adapting code-matrices to the multiclass problems -- 7. Evaluating ensembles of classifiers. 7.1. Generalization error. 7.2. Computational complexity. 7.3. Interpretability of the resulting ensemble. 7.4. Scalability to large datasets. 7.5. Robustness. 7.6. Stability. 7.7. Flexibility. 7.8. Usability. 7.9. Software availability. 7.10. Which ensemble method should be used? Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method Reconnaissance des formes (Informatique) / Classification Théorie des ensembles COMPUTERS / Optical Data Processing bisacsh Mustererkennung swd Pattern recognition systems Algorithms Machine learning Mustererkennung (DE-588)4040936-3 gnd rswk-swf Mustererkennung (DE-588)4040936-3 s 1\p DE-604 World Scientific (Firm) Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=340641 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Rokach, Lior Pattern classification using ensemble methods Reconnaissance des formes (Informatique) / Classification Théorie des ensembles COMPUTERS / Optical Data Processing bisacsh Mustererkennung swd Pattern recognition systems Algorithms Machine learning Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4040936-3 |
| title | Pattern classification using ensemble methods |
| title_auth | Pattern classification using ensemble methods |
| title_exact_search | Pattern classification using ensemble methods |
| title_full | Pattern classification using ensemble methods Lior Rokach |
| title_fullStr | Pattern classification using ensemble methods Lior Rokach |
| title_full_unstemmed | Pattern classification using ensemble methods Lior Rokach |
| title_short | Pattern classification using ensemble methods |
| title_sort | pattern classification using ensemble methods |
| topic | Reconnaissance des formes (Informatique) / Classification Théorie des ensembles COMPUTERS / Optical Data Processing bisacsh Mustererkennung swd Pattern recognition systems Algorithms Machine learning Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Reconnaissance des formes (Informatique) / Classification Théorie des ensembles COMPUTERS / Optical Data Processing Mustererkennung Pattern recognition systems Algorithms Machine learning |
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