GFO: A data driven approach for optimizing the Gaussian function based similarity metric in computational biology

Neurocomputing

Volumn 99, Issue , 2013, Pages 307-315

  Lei, Jian Bo ^a   Yin, Jiang Bo ^a   Shen, Hong Bin ^a  

^a Department of Automation   (China)

Author keywords

Bioinformatics; Gaussian function similarity; GFO; Supervised learning; Unsupervised learning

Indexed keywords

BIOINFORMATICS APPLICATIONS; COMPUTATIONAL BIOLOGY; DATA SET SIZE; DATA SETS; DATA-DRIVEN APPROACH; GAUSSIAN FUNCTIONS; GAUSSIAN KERNELS; GFO; GRID SCALE; GRID SEARCH; OPTIMAL CHOICE; OPTIMAL VALUES; SCALAR PARAMETERS; SIMILARITY METRICS; TRIAL AND ERROR;

GAUSSIAN DISTRIBUTION; LEARNING ALGORITHMS; OPTIMIZATION; SUPERVISED LEARNING; UNSUPERVISED LEARNING;

BIOINFORMATICS;

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; INSTRUMENT VALIDATION; KERNEL METHOD; LEARNING ALGORITHM; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 84867869093     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.07.003     Document Type: Article

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