Neurons With Paraboloid Decision Boundaries for Improved Neural Network Classification Performance

2019 ◽  
Vol 30 (1) ◽  
pp. 284-294 ◽  
Author(s):  
Nikolaos Tsapanos ◽  
Anastasios Tefas ◽  
Nikolaos Nikolaidis ◽  
Ioannis Pitas
Keyword(s):  
Neural Network ◽  
Classification Performance ◽  
Neural Network Classification ◽  
Decision Boundaries

scholarly journals A linear model based on Kalman filter for improving neural network classification performance

2016 ◽  
Vol 49 ◽  
pp. 112-122 ◽  
Author(s):  
Joko Siswantoro ◽  
Anton Satria Prabuwono ◽  
Azizi Abdullah ◽  
Bahari Idrus
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Linear Model ◽  
Classification Performance ◽  
Neural Network Classification ◽  
Model Based

Analysis on Children Custody Decision Making Model

2013 ◽  
Vol 380-384 ◽  
pp. 1247-1255
Author(s):  
Yi Gu ◽  
Guang Ming Zhang
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Weight Vector ◽  
Classification Performance ◽  
Point Of View ◽  
Judicial Practice ◽  
Dynamic Factors ◽  
Custody Disputes ◽  
Legal Expert ◽  
Decision Boundaries

Children custody disputes are complicated for many judges owing to so many dynamic factors should to be considered. This paper proposes a novel model for legal expert system Based on LVQ Neural Network. Firstly, all clauses and discretionary factors, extracted from theories and judicial practice, involved with custody disputes in divorce were quantified to obtain related set elements. Then the LVQ neural network was applied to construct a model for children custody disputes weight vector analysis on the set elements. Ultimately, the weight vector close to decision boundaries between classes could promote the classification performance. Accordingly, the performance of the LVQ by generating weight vectors close to decision boundaries is higher. By testing, custody disputes legal expert system could consider all relevant circumstances from a good all-around point of view and effectively.

HYBRID CLASSIFICATION SCHEMES FOR HEART MURMUR DETECTION TO ASSIST PHONOCARDIOGRAM BASED SIGNAL ACQUISITION

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Solomon Roach
Keyword(s):  
Neural Network ◽  
Signal Acquisition ◽  
Heart Murmur ◽  
Classification Techniques ◽  
Classification Schemes ◽  
Neural Network Classification ◽  
Nonlinear Classification ◽  
Processing Step ◽  
System Characterization ◽  
Hybrid Classification

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying system’s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Cloud Data Set for Neural Network Classification Studies

1992 ◽  
Author(s):  
Rupert S. Hawkins ◽  
K. F. Heideman ◽  
Ira G. Smotroff
Keyword(s):  
Neural Network ◽  
Data Set ◽  
Cloud Data ◽  
Neural Network Classification
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