2014 |
Luckner, Marcin Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. Abstract | Links | BibTeX | Tags: Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines @inproceedings{Luckner2014b, title = {Global and local rejection option in multi-classification task}, author = {Marcin Luckner}, doi = {10.1007/978-3-319-11179-7_61}, issn = {16113349}, year = {2014}, date = {2014-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8681 LNCS}, pages = {483--490}, abstract = {This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland.}, keywords = {Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland. |
Luckner, Marcin Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. Abstract | Links | BibTeX | Tags: Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines @inproceedings{Luckner2014e, title = {Global and local rejection option in multi-classification task}, author = {Marcin Luckner}, doi = {10.1007/978-3-319-11179-7_61}, issn = {16113349}, year = {2014}, date = {2014-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8681 LNCS}, pages = {483--490}, abstract = {This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland.}, keywords = {Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland. |
Publications
2014 |
Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. |
Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. |