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10.1155/2016/6081804 http://scihub22266oqcxt.onion/10.1155/2016/6081804 C4853929!4853929!27195004     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Intell+Neurosci 2016 ; 2016 (ä): ä Nephropedia Template TP {{tp|p=27195004|t=2016. Online Knowledge-Based Model for Big Data Topic Extraction |pdf=|usr=}}{{27195004}} gab.com Text Online Knowledge-Based Model for Big Data Topic Extraction JO: Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=27195004 #FOAvip Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half. Twit Text FOAVip Online Knowledge-Based Model for Big Data Topic Extraction ????Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=27195004 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27195004 #FOAvip English Wikipedia <ref>{{Cite pmid|27195004}}</ref> Online Knowledge-Based Model for Big Data Topic Extraction #MMPMID27195004 Khan MT; Durrani M; Khalid S; Aziz F Comput Intell Neurosci 2016[]; 2016 (ä): ä PMID27195004show ga Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half. äOnline Knowledge-Based Model for Big Data Topic Extraction (epmc).pdf DeepDyve Pubget Overpricing