أعرض تسجيلة المادة بشكل مبسط

dc.contributor Budd, Timothy
dc.contributor Wong, Weng-Keen
dc.contributor Zhang, Eugene
dc.contributor Scott, Michael
dc.date 2007-07-31T16:07:03Z
dc.date 2007-07-31T16:07:03Z
dc.date 2007-06-15
dc.date 2007-07-31T16:07:03Z
dc.date.accessioned 2013-10-16T08:04:35Z
dc.date.available 2013-10-16T08:04:35Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/1957/6226
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1957/6226
dc.description Graduation date: 2008
dc.description A basic tradeoff to consider when designing a distributed data-mining framework is the need for a compromise between the cost of communication and computation resources and the accuracy of the mining results. This is essentially a decision of whether it is more efficient to communicate all of the data to a central site for analysis, possibly increasing the accuracy of the results, or is it more efficient to mine the data locally at each of the remote sites and then combine the results, possibly reducing the use of communication and computation resources. This research attempts the design, analysis, and implementation of an efficient distributed and cumulative learning algorithm with performance guarantees that are provable relative to its centralized or batch counterparts for knowledge acquisition from distributed data sources that will address this tradeoff. This thesis also develops a methodical mathematical framework to describe this type of tradeoff, describes the reduction of the problem to a constrained optimization problem, and demonstrates techniques to balance cost and accuracy levels.
dc.language en_US
dc.subject data mining
dc.subject constrained optimization
dc.title Accuracy versus cost in distributed data mining
dc.type Thesis


الملفات في هذه المادة

الملفات الحجم الصيغة عرض

لا توجد أي ملفات مرتبطة بهذه المادة.

هذه المادة تبدو في المجموعات التالية:

أعرض تسجيلة المادة بشكل مبسط