{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T17:27:30Z","timestamp":1725470850683},"publisher-location":"Berlin, Heidelberg","reference-count":14,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540453758"},{"type":"electronic","value":"9783540460565"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006]]},"DOI":"10.1007\/11871842_37","type":"book-chapter","created":{"date-parts":[[2006,9,18]],"date-time":"2006-09-18T04:28:47Z","timestamp":1158553727000},"page":"377-388","source":"Crossref","is-referenced-by-count":0,"title":["Boosting in PN Spaces"],"prefix":"10.1007","author":[{"given":"Martin","family":"Scholz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Freund, Y., Schapire, R.R.: A decision\u2013theoretic generalization of on-line learning and an application to boosting. Computer and System Sciences\u00a055(1) (1997)","DOI":"10.1006\/jcss.1997.1504"},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"F\u00fcrnkranz, J., Flach, P.: ROC \u2019n\u2019 Rule Learning \u2013 Towards a Better Understanding of Covering Algorithms. Machine Learning\u00a058(1) (2005)","DOI":"10.1007\/s10994-005-5011-x"},{"key":"37_CR3","unstructured":"Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. Tech report HPL-2003-4. HP Laboratories, Palo Alto, CA, USA (2004)"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Schapire, R.E., Singer, Y.: Improved Boosting Using Confidence-rated Predictions. Machine Learning\u00a037(3) (1999)","DOI":"10.1023\/A:1007614523901"},{"key":"37_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/11503415_5","volume-title":"Learning Theory","author":"C. Rudin","year":"2005","unstructured":"Rudin, C., Cortes, C., Mohri, M., Schapire, R.E.: Margin-Based Ranking Meets Boosting in the Middle. In: Auer, P., Meir, R. (eds.) COLT 2005. LNCS, vol.\u00a03559, pp. 63\u201378. Springer, Heidelberg (2005)"},{"key":"37_CR6","unstructured":"Flach, P.A.: The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics. In: Proc. of ICML (2003)"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Rosset, S.: Model Selection via the AUC. In: Proc. of ICML (2004)","DOI":"10.1145\/1015330.1015400"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Friedman, J.H., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. Annals of Statistics\u00a028 (2000)","DOI":"10.1214\/aos\/1016120463"},{"key":"37_CR9","unstructured":"Mason, L., Baxter, J., Bartlett, P., Frean, M.: Boosting algorithms as gradient descent in function space. Technical report, Australian National University (1999)"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Scholz, M.: Sampling-Based Sequential Subgroup Mining. In: Proc. of KDD (2005)","DOI":"10.1145\/1081870.1081902"},{"key":"37_CR11","unstructured":"Blake, C., Merz, C.: UCI Repository of machine learning databases (1998)"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proc. of KDD (2006)","DOI":"10.1145\/1150402.1150531"},{"key":"37_CR13","volume-title":"Data Mining \u2013 Practical Machine Learning Tools and Techniques with Java Implementations","author":"I. Witten","year":"2000","unstructured":"Witten, I., Frank, E.: Data Mining \u2013 Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)"},{"key":"37_CR14","unstructured":"Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proc. of ICML (1999)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning: ECML 2006"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/11871842_37.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T14:47:02Z","timestamp":1605624422000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/11871842_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006]]},"ISBN":["9783540453758","9783540460565"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/11871842_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2006]]}}}