Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 143
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3098
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
Message: Attempt to read property "Count" on bool
Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3100
Function: _error_handler
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
A key challenge in many applications of multisource transfer learning is to explicitly capture the diverse source-target similarities. In this article, we are concerned with stretching the set of practical approaches based on Gaussian process (GP) models to solve multisource transfer regression problems. Precisely, we first investigate the feasibility and performance of a family of transfer covariance functions that represent the pairwise similarity of each source and the target domain. We theoretically show that using such a transfer covariance function for general GP modeling can only capture the same similarity coefficient for all the sources, and thus may result in unsatisfactory transfer performance. This outcome, together with the scalability issues of a single GP based approach, leads us to propose TCStack , an integrated framework incorporating a separate transfer covariance function for each source and stacking. Contrary to typical stacking approaches, TCStack learns the source-target similarity in each base GP model by considering the dependencies of the other sources along the process. We introduce two instances of the proposed TCStack . Extensive experiments on one synthetic and two real-world data sets, with learning settings up to 11 sources for the latter, demonstrate the effectiveness of our approach.
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Source |
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http://dx.doi.org/10.1109/TNNLS.2020.3012457 | DOI Listing |
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