| dc.contributor | 統計系 | zh_Tw |
| dc.creator (作者) | 周珮婷 | zh_TW |
| dc.creator (作者) | Chou, Elizabeth P. | en_US |
| dc.date (日期) | 2015-07 | en_US |
| dc.date.accessioned | 8-Aug-2017 16:59:37 (UTC+8) | - |
| dc.date.available | 8-Aug-2017 16:59:37 (UTC+8) | - |
| dc.date.issued (上傳時間) | 8-Aug-2017 16:59:37 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/111675 | - |
| dc.description.abstract (摘要) | High dimensional covariate information provides a detailed description of any individuals involved in a machine learning and classification problem. The inter-dependence patterns among these covariate vectors may be unknown to researchers. This fact is not well recognized in classic and modern machine learning literature; most model-based popular algorithms are implemented using some version of the dimensionreduction approach or even impose a built-in complexity penalty. This is a defensive attitude toward the high dimensionality. In contrast, an accommodating attitude can exploit such potential inter-dependence patterns embedded within the high dimensionality. In this research, we implement this latter attitude throughout by first computing the similarity between data nodes and then discovering pattern information in the form of Ultrametric tree geometry among almost all the covariate dimensions involved. We illustrate with real Microarray datasets, where we demonstrate that such dual-relationships are indeed class specific, each precisely representing the discovery of a biomarker. The whole collection of computed biomarkers constitutes a global feature-matrix, which is then shown to give rise to a very effective learning algorithm. © Springer International Publishing Switzerland 2015. | en_US |
| dc.format.extent | 129522 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9166, 395-402 | en_US |
| dc.relation (關聯) | 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015; Hamburg; Germany; 20 July 2015 到 21 July 2015; 代碼 156629 | en_US |
| dc.subject (關鍵詞) | Artificial intelligence; Biomarkers; Classification (of information); Data mining; Geometry; Learning systems; Microarrays; Pattern recognition; Supervised learning; Trees (mathematics); BiDCG; Complexity penalties; Data clouds; Dimension-reduction; High dimensionality; Microarray data sets; Pattern information; Semi- supervised learning; Learning algorithms | en_US |
| dc.title (題名) | Data driven geometry for learning | en_US |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1007/978-3-319-21024-7_27 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/978-3-319-21024-7_27 | |
