Classification and regression trees:
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York ; Washington, D.C.
Chapman & Hall/CRC
[1984]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | x, 358 Seiten Diagramme |
| ISBN: | 9780412048418 |
Internformat
MARC
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| 100 | 1 | |a Breiman, Leo |d 1928-2005 |e Verfasser |0 (DE-588)136421229 |4 aut | |
| 245 | 1 | 0 | |a Classification and regression trees |c Leo Breiman (University of California, Berkeley), Jerome H. Friedman (Stanford University), Richard A. Olshen (Stanford University), Charles J. Stone (University of California, Berkeley) |
| 264 | 1 | |a Boca Raton ; London ; New York ; Washington, D.C. |b Chapman & Hall/CRC |c [1984] | |
| 300 | |a x, 358 Seiten |b Diagramme | ||
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| 650 | 4 | |a Analyse de régression | |
| 650 | 7 | |a Analyse de régression |2 ram | |
| 650 | 4 | |a Analyse discriminante | |
| 650 | 7 | |a Analyse discriminante |2 ram | |
| 650 | 4 | |a Arbres (Théorie des graphes) | |
| 650 | 7 | |a Arbres (théorie des graphes) |2 ram | |
| 650 | 7 | |a analyse discriminante |2 inriac | |
| 650 | 7 | |a analyse régression |2 inriac | |
| 650 | 7 | |a arbre régression |2 inriac | |
| 650 | 7 | |a arbre |2 inriac | |
| 650 | 7 | |a classification |2 inriac | |
| 650 | 7 | |a régression |2 inriac | |
| 650 | 4 | |a Discriminant analysis | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Trees (Graph theory) | |
| 700 | 1 | |a Friedman, Jerome H. |d 1939- |e Verfasser |0 (DE-588)134071484 |4 aut | |
| 700 | 1 | |a Olshen, Richard A. |d 1942- |e Verfasser |0 (DE-588)1221612948 |4 aut | |
| 700 | 1 | |a Stone, Charles J. |d 1936- |e Verfasser |0 (DE-588)1221613278 |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804186455528964096 |
|---|---|
| adam_text | CONTENTS Preface Chapter 1 1.1 1.2 1.3 1.4 1.5 INTRODUCTION TOTREECLASSIFICATION The Ship Classification Problem Tree Structured Classifiers Construction of the Tree Classifier Initial Tree Growing Methodology Methodological Development Two Running Examples The Advantages of the Tree Structured Approach Chapter 3 3.1 3.2 3.3 3.4 3.5 BACKGROUND Classifiers as Partitions Use of Data in Constructing Classifiers The Purposes of Classification Analysis Estimating Accuracy The Bayes Rule and Current Classification Procedures Chapter 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 viii RIGHT SIZEDTREES AND HONESTESTIMATES Introduction Getting Ready to Prune Minimal Cost-Complexity Pruning The Best-Pruned Subtree: An Estimation Problem Some Examples Appendix 1 2 4 6 8 13 18 18 20 23 27 36 43 55 59 59 63 66 72 81 87
vi Contents Chapter 4 4.1 4.2 4.3 4.4 4.5 4.6 Reducing Misclassification Cost The Two-Class Problem The Multiclass Problem: Unit Costs Priors and Variable Misclassification Costs Two Examples Class Probability Trees Via Gini Appendix Chapter 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 93 94 98 103 112 115 121 126 130 130 131 140 150 155 163 168 171 174 175 182 189 194 201 MASS SPECTRA CLASSIFICATION 203 Introduction Generalized Tree Construction The Bromine Tree: A Nonstandard Example 203 205 205 Chapter 8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 MEDICAL DIAGNOSIS AND PROGNOSIS Prognosis After Heart Attack Diagnosing Heart Attacks Immunosuppression and the Diagnosis of Cancer Gait Analysis and the Detection of Outliers Related Work on Computer-Aided Diagnosis Chapter 7 7.1 7.2 7.3 STRENGTHENING AND INTERPRETING Introduction Variable Combinations Surrogate Splits and Their Uses Estimating Within-Node Cost Interpretation and Exploration Computational Efficiency Comparison of Accuracy with Other Methods Appendix Chapter 6 6.1 6.2 6.3 6.4 6.5 SPLITTING RULES REGRESSION TREES Introduction An Example Least Squares Regression Tree Structured Regression Pruning and Estimating A Simulated Example Two Cross-Validation Issues Standard Structure Trees 216 217 221 228 232 237 247
vii Contents 8.9 8.10 8.11 8.12 Using Surrogate Splits Interpretation Least Absolute DeviationRegression Overall Conclusions Chapter 9 9.1 9.2 9.3 9.4 BAYES RULES AND PARTITIONS Bayes Rule Bayes Rule for a Partition Risk Reduction Splitting Rule Categorical Splits Chapter 10 10.1 10.2 10.3 Chapter 11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 CONSTRUCTION OF TREESFROM A LEARNING SAMPLE Estimated Bayes Rule fora Partition Empirical Risk ReductionSplitting Rule Optimal Pruning Test Samples Cross-Validation Final Tree Selection Bootstrap Estimate of Overall Risk End-Cut Preference 266 266 269 272 274 279 279 284 293 297 298 300 302 303 306 309 311 313 CONSISTENCY 318 Empirical Distributions Regression Classification Proofs for Section 12.1 Proofs for Section 12.2 Proofs for Section 12.3 319 321 324 327 332 337 Chapter 12 12.1 12.2 12.3 12.4 12.5 12.6 OPTIMAL PRUNING Tree Terminology Optimally Pruned Subtrees An Explicit Optimal Pruning Algorithm 248 251 255 264 Bibliography 342 Notation Index 347 Subject Index 354
|
| adam_txt |
CONTENTS Preface Chapter 1 1.1 1.2 1.3 1.4 1.5 INTRODUCTION TOTREECLASSIFICATION The Ship Classification Problem Tree Structured Classifiers Construction of the Tree Classifier Initial Tree Growing Methodology Methodological Development Two Running Examples The Advantages of the Tree Structured Approach Chapter 3 3.1 3.2 3.3 3.4 3.5 BACKGROUND Classifiers as Partitions Use of Data in Constructing Classifiers The Purposes of Classification Analysis Estimating Accuracy The Bayes Rule and Current Classification Procedures Chapter 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 viii RIGHT SIZEDTREES AND HONESTESTIMATES Introduction Getting Ready to Prune Minimal Cost-Complexity Pruning The Best-Pruned Subtree: An Estimation Problem Some Examples Appendix 1 2 4 6 8 13 18 18 20 23 27 36 43 55 59 59 63 66 72 81 87
vi Contents Chapter 4 4.1 4.2 4.3 4.4 4.5 4.6 Reducing Misclassification Cost The Two-Class Problem The Multiclass Problem: Unit Costs Priors and Variable Misclassification Costs Two Examples Class Probability Trees Via Gini Appendix Chapter 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 93 94 98 103 112 115 121 126 130 130 131 140 150 155 163 168 171 174 175 182 189 194 201 MASS SPECTRA CLASSIFICATION 203 Introduction Generalized Tree Construction The Bromine Tree: A Nonstandard Example 203 205 205 Chapter 8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 MEDICAL DIAGNOSIS AND PROGNOSIS Prognosis After Heart Attack Diagnosing Heart Attacks Immunosuppression and the Diagnosis of Cancer Gait Analysis and the Detection of Outliers Related Work on Computer-Aided Diagnosis Chapter 7 7.1 7.2 7.3 STRENGTHENING AND INTERPRETING Introduction Variable Combinations Surrogate Splits and Their Uses Estimating Within-Node Cost Interpretation and Exploration Computational Efficiency Comparison of Accuracy with Other Methods Appendix Chapter 6 6.1 6.2 6.3 6.4 6.5 SPLITTING RULES REGRESSION TREES Introduction An Example Least Squares Regression Tree Structured Regression Pruning and Estimating A Simulated Example Two Cross-Validation Issues Standard Structure Trees 216 217 221 228 232 237 247
vii Contents 8.9 8.10 8.11 8.12 Using Surrogate Splits Interpretation Least Absolute DeviationRegression Overall Conclusions Chapter 9 9.1 9.2 9.3 9.4 BAYES RULES AND PARTITIONS Bayes Rule Bayes Rule for a Partition Risk Reduction Splitting Rule Categorical Splits Chapter 10 10.1 10.2 10.3 Chapter 11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 CONSTRUCTION OF TREESFROM A LEARNING SAMPLE Estimated Bayes Rule fora Partition Empirical Risk ReductionSplitting Rule Optimal Pruning Test Samples Cross-Validation Final Tree Selection Bootstrap Estimate of Overall Risk End-Cut Preference 266 266 269 272 274 279 279 284 293 297 298 300 302 303 306 309 311 313 CONSISTENCY 318 Empirical Distributions Regression Classification Proofs for Section 12.1 Proofs for Section 12.2 Proofs for Section 12.3 319 321 324 327 332 337 Chapter 12 12.1 12.2 12.3 12.4 12.5 12.6 OPTIMAL PRUNING Tree Terminology Optimally Pruned Subtrees An Explicit Optimal Pruning Algorithm 248 251 255 264 Bibliography 342 Notation Index 347 Subject Index 354 |
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| isbn | 9780412048418 |
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| spelling | Breiman, Leo 1928-2005 Verfasser (DE-588)136421229 aut Classification and regression trees Leo Breiman (University of California, Berkeley), Jerome H. Friedman (Stanford University), Richard A. Olshen (Stanford University), Charles J. Stone (University of California, Berkeley) Boca Raton ; London ; New York ; Washington, D.C. Chapman & Hall/CRC [1984] x, 358 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Analyse de régression Analyse de régression ram Analyse discriminante Analyse discriminante ram Arbres (Théorie des graphes) Arbres (théorie des graphes) ram analyse discriminante inriac analyse régression inriac arbre régression inriac arbre inriac classification inriac régression inriac Discriminant analysis Regression analysis Trees (Graph theory) Friedman, Jerome H. 1939- Verfasser (DE-588)134071484 aut Olshen, Richard A. 1942- Verfasser (DE-588)1221612948 aut Stone, Charles J. 1936- Verfasser (DE-588)1221613278 aut Äquivalent Druck-Ausgabe, Hardcover 978-1-138-46952-5 Erscheint auch als Online-Ausgabe 978-1-3514-6048-4 Erscheint auch als Online-Ausgabe 978-1-3151-3947-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034931468&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Breiman, Leo 1928-2005 Friedman, Jerome H. 1939- Olshen, Richard A. 1942- Stone, Charles J. 1936- Classification and regression trees Analyse de régression Analyse de régression ram Analyse discriminante Analyse discriminante ram Arbres (Théorie des graphes) Arbres (théorie des graphes) ram analyse discriminante inriac analyse régression inriac arbre régression inriac arbre inriac classification inriac régression inriac Discriminant analysis Regression analysis Trees (Graph theory) |
| title | Classification and regression trees |
| title_auth | Classification and regression trees |
| title_exact_search | Classification and regression trees |
| title_exact_search_txtP | Classification and regression trees |
| title_full | Classification and regression trees Leo Breiman (University of California, Berkeley), Jerome H. Friedman (Stanford University), Richard A. Olshen (Stanford University), Charles J. Stone (University of California, Berkeley) |
| title_fullStr | Classification and regression trees Leo Breiman (University of California, Berkeley), Jerome H. Friedman (Stanford University), Richard A. Olshen (Stanford University), Charles J. Stone (University of California, Berkeley) |
| title_full_unstemmed | Classification and regression trees Leo Breiman (University of California, Berkeley), Jerome H. Friedman (Stanford University), Richard A. Olshen (Stanford University), Charles J. Stone (University of California, Berkeley) |
| title_short | Classification and regression trees |
| title_sort | classification and regression trees |
| topic | Analyse de régression Analyse de régression ram Analyse discriminante Analyse discriminante ram Arbres (Théorie des graphes) Arbres (théorie des graphes) ram analyse discriminante inriac analyse régression inriac arbre régression inriac arbre inriac classification inriac régression inriac Discriminant analysis Regression analysis Trees (Graph theory) |
| topic_facet | Analyse de régression Analyse discriminante Arbres (Théorie des graphes) Arbres (théorie des graphes) analyse discriminante analyse régression arbre régression arbre classification régression Discriminant analysis Regression analysis Trees (Graph theory) |
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