scholarly journals Erratum to: Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals

2017 ◽  
pp. E1-E1
Author(s):  
Xueyuan Gong ◽  
Simon Fong ◽  
Yain-Whar Si ◽  
Robert P. Biuk-Aghai ◽  
Raymond K. Wong ◽  
...  
Keyword(s):  
Pattern Discovery ◽  
Cross Match

Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals

2016 ◽  
pp. 169-180 ◽  
Author(s):  
Xueyuan Gong ◽  
Simon Fong ◽  
Yain-Whar Si ◽  
Robert P. Biuk-Aghai ◽  
Raymond K. Wong ◽  
...  
Keyword(s):  
Pattern Discovery ◽  
Cross Match

Parallel massive data oriented astronomical cross-match

2010 ◽  
Vol 30 (8) ◽  
pp. 2056-2059 ◽  
Author(s):  
Qing ZHAO ◽  
Ji-zhou SUN ◽  
Ce YU ◽  
Chen-zhou CUI ◽  
Jian XIAO
Keyword(s):  
Massive Data ◽  
Cross Match

scholarly journals Pattern discovery and disentanglement on relational datasets

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew K. C. Wong ◽  
Pei-Yuan Zhou ◽  
Zahid A. Butt
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Case Studies ◽  
Prediction Accuracy ◽  
Pattern Discovery ◽  
Explicit Representation ◽  
Human Cognition ◽  
Source Level ◽  
Low Volume

AbstractMachine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.

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