@inProceedings{
 title = {Modeling the temperature of hot rolled steel plate with semisupervised learning methods.},
 type = {inProceedings},
 year = {2011},
 pages = {351-364},
 volume = {6926},
 publisher = {Springer},
 id = {c30ba8ca-95d6-3fb8-a30e-5e92f62b9c8c},
 created = {2019-11-19T13:01:08.851Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:48:26.789Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {isg:1591},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {The semi-supervised learning methods utilize both the labeled and unlabeled data to produce better learners than the usual methods using only the labeled data. In this study, semi-supervised learning is applied to the modeling of the rolling temperature of steel plate. Measurement of the rolling temperature in the extreme conditions of rolling mill is difficult and thus there is a large amount of missing response measurements. Previous research mainly
focuses on semi-supervised classification. Application of semi-supervised learning to regression problems is largely understudied. Co-training is a semi-supervised method, which is promising in the semi-supervised regression setting. In this paper, we used COREG algorithm [10] to a data set collected from steel plate rolling. Our results show that COREG can effectively exploit unlabeled data and improves the prediction accuracy. The achieved prediction
accuracy 16◦C is a major improvement in comparison to the earlier approach in which temperature is predicted using physical-mathematical models. In addition, features that describe the rolling process and are applicable to input variables of learning methods are presented. The results can be utilized to develop statistical models for temperature prediction for other rolling processes as well.},
 bibtype = {inProceedings},
 author = {Tiensuu H, Juutilainen I & Röning J},
 booktitle = {Proc. 14th International Conference on Discovery Science (DS 2011), Lecture Notes in Computer Science}
}

{"_id":"AfsXuNyDje9CYTud6","bibbaseid":"tiensuuh-modelingthetemperatureofhotrolledsteelplatewithsemisupervisedlearningmethods-2011","authorIDs":[],"author_short":["Tiensuu H, J., I., &., R., J."],"bibdata":{"title":"Modeling the temperature of hot rolled steel plate with semisupervised learning methods.","type":"inProceedings","year":"2011","pages":"351-364","volume":"6926","publisher":"Springer","id":"c30ba8ca-95d6-3fb8-a30e-5e92f62b9c8c","created":"2019-11-19T13:01:08.851Z","file_attached":false,"profile_id":"bddcf02d-403b-3b06-9def-6d15cc293e20","group_id":"17585b85-df99-3a34-98c2-c73e593397d7","last_modified":"2019-11-19T13:48:26.789Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"isg:1591","source_type":"inproceedings","private_publication":false,"abstract":"The semi-supervised learning methods utilize both the labeled and unlabeled data to produce better learners than the usual methods using only the labeled data. In this study, semi-supervised learning is applied to the modeling of the rolling temperature of steel plate. Measurement of the rolling temperature in the extreme conditions of rolling mill is difficult and thus there is a large amount of missing response measurements. Previous research mainly\nfocuses on semi-supervised classification. Application of semi-supervised learning to regression problems is largely understudied. Co-training is a semi-supervised method, which is promising in the semi-supervised regression setting. In this paper, we used COREG algorithm [10] to a data set collected from steel plate rolling. Our results show that COREG can effectively exploit unlabeled data and improves the prediction accuracy. The achieved prediction\naccuracy 16◦C is a major improvement in comparison to the earlier approach in which temperature is predicted using physical-mathematical models. In addition, features that describe the rolling process and are applicable to input variables of learning methods are presented. The results can be utilized to develop statistical models for temperature prediction for other rolling processes as well.","bibtype":"inProceedings","author":"Tiensuu H, Juutilainen I & Röning J","booktitle":"Proc. 14th International Conference on Discovery Science (DS 2011), Lecture Notes in Computer Science","bibtex":"@inProceedings{\n title = {Modeling the temperature of hot rolled steel plate with semisupervised learning methods.},\n type = {inProceedings},\n year = {2011},\n pages = {351-364},\n volume = {6926},\n publisher = {Springer},\n id = {c30ba8ca-95d6-3fb8-a30e-5e92f62b9c8c},\n created = {2019-11-19T13:01:08.851Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n group_id = {17585b85-df99-3a34-98c2-c73e593397d7},\n last_modified = {2019-11-19T13:48:26.789Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {isg:1591},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The semi-supervised learning methods utilize both the labeled and unlabeled data to produce better learners than the usual methods using only the labeled data. In this study, semi-supervised learning is applied to the modeling of the rolling temperature of steel plate. Measurement of the rolling temperature in the extreme conditions of rolling mill is difficult and thus there is a large amount of missing response measurements. Previous research mainly\nfocuses on semi-supervised classification. Application of semi-supervised learning to regression problems is largely understudied. Co-training is a semi-supervised method, which is promising in the semi-supervised regression setting. In this paper, we used COREG algorithm [10] to a data set collected from steel plate rolling. Our results show that COREG can effectively exploit unlabeled data and improves the prediction accuracy. The achieved prediction\naccuracy 16◦C is a major improvement in comparison to the earlier approach in which temperature is predicted using physical-mathematical models. In addition, features that describe the rolling process and are applicable to input variables of learning methods are presented. The results can be utilized to develop statistical models for temperature prediction for other rolling processes as well.},\n bibtype = {inProceedings},\n author = {Tiensuu H, Juutilainen I & Röning J},\n booktitle = {Proc. 14th International Conference on Discovery Science (DS 2011), Lecture Notes in Computer Science}\n}","author_short":["Tiensuu H, J., I., &., R., J."],"bibbaseid":"tiensuuh-modelingthetemperatureofhotrolledsteelplatewithsemisupervisedlearningmethods-2011","role":"author","urls":{},"downloads":0},"bibtype":"inProceedings","creationDate":"2019-11-19T13:17:06.377Z","downloads":0,"keywords":[],"search_terms":["modeling","temperature","hot","rolled","steel","plate","semisupervised","learning","methods","tiensuu h"],"title":"Modeling the temperature of hot rolled steel plate with semisupervised learning methods.","year":2011}