Learning with feature partitions
| buir.advisor | Güvenir, Halil Altay | |
| dc.contributor.author | Şirin, İzzet | |
| dc.date.accessioned | 2016-01-08T20:10:56Z | |
| dc.date.available | 2016-01-08T20:10:56Z | |
| dc.date.issued | 1993 | |
| dc.department | Department of Computer Engineering | en_US |
| dc.description | Ankara : Department of Computer Engineering and Information Science and Institute of Engineering and Science, Bilkent Univ., 1993. | en_US |
| dc.description | Thesis (Master's) -- Bilkent University, 1993. | en_US |
| dc.description | Includes bibliographical references leaves 78-81 | en_US |
| dc.description.abstract | This thesis presents a new methodology of learning from examples, based on feature partitioning. Classification by Feature Partitioning (CFP) is a particular implementation of this technique, which is an inductive, incremental, and supervised learning method. Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition, a basic unit of representation which is initially a point in the feature dimension, is expanded through generalization. The CFP algorithm specializes a partition by subdividing it into two subpartitions. Theoretical (with respect to PAC-model) and empirical evaluation of the CFP is presented and compared with some other similar techniques. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.description.statementofresponsibility | Şirin, İzzet | en_US |
| dc.format.extent | xii, 88 leaves | en_US |
| dc.identifier.uri | http://hdl.handle.net/11693/17508 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | inductive learning | en_US |
| dc.subject | incremental learning | en_US |
| dc.subject | supervised learning | en_US |
| dc.subject | feature partitioning | en_US |
| dc.subject.lcc | Q325.5 .S57 1993 | en_US |
| dc.subject.lcsh | Machine learning. | en_US |
| dc.subject.lcsh | Inductive learning. | en_US |
| dc.subject.lcsh | Supervised learning (machine learning). | en_US |
| dc.subject.lcsh | Supervised learning. | en_US |
| dc.title | Learning with feature partitions | en_US |
| dc.type | Thesis | en_US |
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