Spherical Learning Theory View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2008-2011

FUNDING AMOUNT

300000 CNY

ABSTRACT

In many applied problems such as seismology, earth measure and cerebral medical simulation, data is often collected on a spherical surface. So people try to look for a functional model with “machine architecture” to handle analysis and manufacture those data collected to get the information needed. Based on machine learning of data is the most important technology in information handling and one of the hot topic in informatics, this project researched on the following core problems such as construction of learning machine, learning theory and algorithm and data classification on a spherical surface thoroughly and systematically. It absorbed and developed some important theory and algorithm of harmonic analysis, function approximation and statistics, established a general architecture method for the neural network and support vector machine on a spherical surface, and studied the theory and method of spherical surface learning rate estimation variously to construct the theory and algorithm of spherical surface learning. Hence the accomplishment of this project not only provided theory and method for applied problems, but also developed and enriched learning theory, which promoted the development of cross subjects. Finishing the item results in that forty-seven papers have been published, where the twenty-four pap More... »

URL

http://npd.nsfc.gov.cn/projectDetail.action?pid=60873206

Related SciGraph Publications

  • 2011-12. Strong Converse Inequality for a Spherical Operator in JOURNAL OF INEQUALITIES AND APPLICATIONS
  • 2009-12. The Direct and Converse Inequalities for Jackson-Type Operators on Spherical Cap in JOURNAL OF INEQUALITIES AND APPLICATIONS
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