Evaluating Learning Algorithms: a classification perspective
The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book exami...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2011
|
| Subjects: | |
| Online Access: | BSB01 FHN01 UPA01 Volltext |
| Summary: | The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings |
| Item Description: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Physical Description: | 1 online resource (xvi, 406 pages) |
| ISBN: | 9780511921803 |
| DOI: | 10.1017/CBO9780511921803 |
Staff View
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV043945640 | ||
| 003 | DE-604 | ||
| 005 | 20190410 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 161206s2011 |||| o||u| ||||||eng d | ||
| 020 | |a 9780511921803 |c Online |9 978-0-511-92180-3 | ||
| 024 | 7 | |a 10.1017/CBO9780511921803 |2 doi | |
| 035 | |a (ZDB-20-CBO)CR9780511921803 | ||
| 035 | |a (OCoLC)838983295 | ||
| 035 | |a (DE-599)BVBBV043945640 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-12 |a DE-92 |a DE-739 | ||
| 082 | 0 | |a 006.3/1 |2 22 | |
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 100 | 1 | |a Japkowicz, Nathalie |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Evaluating Learning Algorithms |b a classification perspective |c Nathalie Japkowicz, Mohak Shah |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2011 | |
| 300 | |a 1 online resource (xvi, 406 pages) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
| 505 | 8 | |a 1. Introduction -- 2. Machine Learning and Statistics Overview -- 3. Performance Measures I -- 4. Performance Measures II -- 5. Error Estimation -- 6. Statistical Significance testing --7. Datasets and Experimental Framework --8. Recent Developments -- 9. Conclusion -- Appendix A: Statistical Tables -- Appendix B: Additional Information on the Data -- Appendix C: Two Case Studies | |
| 520 | |a The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings | ||
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Computer algorithms / Evaluation | |
| 650 | 0 | 7 | |a Klassifikation |0 (DE-588)4030958-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Algorithmus |0 (DE-588)4001183-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Algorithmus |0 (DE-588)4001183-5 |D s |
| 689 | 0 | 2 | |a Klassifikation |0 (DE-588)4030958-7 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Shah, Mohak |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druckausgabe |z 978-0-521-19600-0 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druckausgabe |z 978-1-107-65311-5 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/CBO9780511921803 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-20-CBO | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-029354611 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 966 | e | |u https://doi.org/10.1017/CBO9780511921803 |l BSB01 |p ZDB-20-CBO |q BSB_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/CBO9780511921803 |l FHN01 |p ZDB-20-CBO |q FHN_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/CBO9780511921803 |l UPA01 |p ZDB-20-CBO |q UPA_PDA_CBO_Kauf |x Verlag |3 Volltext | |
Record in the Search Index
| _version_ | 1804176892088025088 |
|---|---|
| any_adam_object | |
| author | Japkowicz, Nathalie |
| author_facet | Japkowicz, Nathalie |
| author_role | aut |
| author_sort | Japkowicz, Nathalie |
| author_variant | n j nj |
| building | Verbundindex |
| bvnumber | BV043945640 |
| classification_rvk | ST 300 |
| collection | ZDB-20-CBO |
| contents | 1. Introduction -- 2. Machine Learning and Statistics Overview -- 3. Performance Measures I -- 4. Performance Measures II -- 5. Error Estimation -- 6. Statistical Significance testing --7. Datasets and Experimental Framework --8. Recent Developments -- 9. Conclusion -- Appendix A: Statistical Tables -- Appendix B: Additional Information on the Data -- Appendix C: Two Case Studies |
| ctrlnum | (ZDB-20-CBO)CR9780511921803 (OCoLC)838983295 (DE-599)BVBBV043945640 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1017/CBO9780511921803 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03737nmm a2200553zc 4500</leader><controlfield tag="001">BV043945640</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20190410 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">161206s2011 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780511921803</subfield><subfield code="c">Online</subfield><subfield code="9">978-0-511-92180-3</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/CBO9780511921803</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9780511921803</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)838983295</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV043945640</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/1</subfield><subfield code="2">22</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Japkowicz, Nathalie</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluating Learning Algorithms</subfield><subfield code="b">a classification perspective</subfield><subfield code="c">Nathalie Japkowicz, Mohak Shah</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2011</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xvi, 406 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Title from publisher's bibliographic system (viewed on 05 Oct 2015)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1. Introduction -- 2. Machine Learning and Statistics Overview -- 3. Performance Measures I -- 4. Performance Measures II -- 5. Error Estimation -- 6. Statistical Significance testing --7. Datasets and Experimental Framework --8. Recent Developments -- 9. Conclusion -- Appendix A: Statistical Tables -- Appendix B: Additional Information on the Data -- Appendix C: Two Case Studies</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer algorithms / Evaluation</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Klassifikation</subfield><subfield code="0">(DE-588)4030958-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Algorithmus</subfield><subfield code="0">(DE-588)4001183-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Algorithmus</subfield><subfield code="0">(DE-588)4001183-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Klassifikation</subfield><subfield code="0">(DE-588)4030958-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shah, Mohak</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druckausgabe</subfield><subfield code="z">978-0-521-19600-0</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druckausgabe</subfield><subfield code="z">978-1-107-65311-5</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/CBO9780511921803</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CBO</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-029354611</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511921803</subfield><subfield code="l">BSB01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511921803</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511921803</subfield><subfield code="l">UPA01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">UPA_PDA_CBO_Kauf</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV043945640 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:24Z |
| institution | BVB |
| isbn | 9780511921803 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029354611 |
| oclc_num | 838983295 |
| open_access_boolean | |
| owner | DE-12 DE-92 DE-739 |
| owner_facet | DE-12 DE-92 DE-739 |
| physical | 1 online resource (xvi, 406 pages) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO ZDB-20-CBO UPA_PDA_CBO_Kauf |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Japkowicz, Nathalie Verfasser aut Evaluating Learning Algorithms a classification perspective Nathalie Japkowicz, Mohak Shah Cambridge Cambridge University Press 2011 1 online resource (xvi, 406 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) 1. Introduction -- 2. Machine Learning and Statistics Overview -- 3. Performance Measures I -- 4. Performance Measures II -- 5. Error Estimation -- 6. Statistical Significance testing --7. Datasets and Experimental Framework --8. Recent Developments -- 9. Conclusion -- Appendix A: Statistical Tables -- Appendix B: Additional Information on the Data -- Appendix C: Two Case Studies The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings Machine learning Computer algorithms / Evaluation Klassifikation (DE-588)4030958-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Algorithmus (DE-588)4001183-5 s Klassifikation (DE-588)4030958-7 s 1\p DE-604 Shah, Mohak Sonstige oth Erscheint auch als Druckausgabe 978-0-521-19600-0 Erscheint auch als Druckausgabe 978-1-107-65311-5 https://doi.org/10.1017/CBO9780511921803 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Japkowicz, Nathalie Evaluating Learning Algorithms a classification perspective 1. Introduction -- 2. Machine Learning and Statistics Overview -- 3. Performance Measures I -- 4. Performance Measures II -- 5. Error Estimation -- 6. Statistical Significance testing --7. Datasets and Experimental Framework --8. Recent Developments -- 9. Conclusion -- Appendix A: Statistical Tables -- Appendix B: Additional Information on the Data -- Appendix C: Two Case Studies Machine learning Computer algorithms / Evaluation Klassifikation (DE-588)4030958-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4030958-7 (DE-588)4193754-5 (DE-588)4001183-5 |
| title | Evaluating Learning Algorithms a classification perspective |
| title_auth | Evaluating Learning Algorithms a classification perspective |
| title_exact_search | Evaluating Learning Algorithms a classification perspective |
| title_full | Evaluating Learning Algorithms a classification perspective Nathalie Japkowicz, Mohak Shah |
| title_fullStr | Evaluating Learning Algorithms a classification perspective Nathalie Japkowicz, Mohak Shah |
| title_full_unstemmed | Evaluating Learning Algorithms a classification perspective Nathalie Japkowicz, Mohak Shah |
| title_short | Evaluating Learning Algorithms |
| title_sort | evaluating learning algorithms a classification perspective |
| title_sub | a classification perspective |
| topic | Machine learning Computer algorithms / Evaluation Klassifikation (DE-588)4030958-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Machine learning Computer algorithms / Evaluation Klassifikation Maschinelles Lernen Algorithmus |
| url | https://doi.org/10.1017/CBO9780511921803 |
| work_keys_str_mv | AT japkowicznathalie evaluatinglearningalgorithmsaclassificationperspective AT shahmohak evaluatinglearningalgorithmsaclassificationperspective |