Both the learning model and the learning process of CSPL are customized to different query instances. CSPL can make use of the characteristics of the query instance to explore a focused hypothesis space effectively during classification. Unlike many existing learning methods, CSPL conducts learning from specific to general, effectively avoiding the horizon effect. Empirical investigation demonstrates that learning from specific to general can discover more useful patterns for learning. Experimental results on benchmark data sets and real-world problems demonstrate that our CSPL framework has a prominent learning performance in comparison with existing learning rnethods. / CSPL integrates the attributes and instances in a query matrix model under customized learning framework. Within this query matrix model, it can be demonstrated that attributes and instances have a useful symmetry property for learning. This symmetry property leads to a solution for counteracting the negative factor of sparse instances with the abundance of attribute information, which was previously viewed as a kind of dimension curse for common learning methods. Given this symmetry property, we propose to use support patterns as the basic learning unit of CSPL, i.e., the patterns to be explored. Generally, a support pattern can be viewed as a sub-matrix of the query matrix, considering its associated support instances and attribute values. CSPL discovers useful support patterns and combines their statistics for classifying unseen instances. / The developing of machine learning techniques still has a number of challenges. Real world problems often require a more flexible and dynamic learning method, which is customized to the learning scenario and user demand. For example, it is quite often in real-world applications to make a critical decision with only limited data but huge amount of potentially relevant attributes. Therefore, we propose a novel customized learning framework called Customized Support Pattern Learner (CSPL), which exploits a tradeoff between instance-based learning and attribute-based learning. / Han Yiqiu. / "October 2005." / Adviser: Wai Lam. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3898. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 99-104). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_343663 |
| Date | January 2005 |
| Contributors | Han, Yiqiu., Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (ix, 104 p. : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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