On Generalized Decision Functions: Reducts, Networks and Ensembles

2015 ◽  
pp. 13-23 ◽  
Author(s):  
Dominik Ślęzak
Keyword(s):  
Decision Functions

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

Seizure Detection: An Assessment of Time- and Frequency-Based Features in a Unified Two-Dimensional Decisional Space Using Nonlinear Decision Functions

2009 ◽  
Vol 26 (6) ◽  
pp. 381-391 ◽  
Author(s):  
Maria Tito ◽  
Mercedes Cabrerizo ◽  
Melvin Ayala ◽  
Prasanna Jayakar ◽  
Malek Adjouadi
Keyword(s):  
Seizure Detection ◽  
Two Dimensional ◽  
Decision Functions

Social Decision Functions

1999 ◽  
pp. 45-122
Author(s):  
Fuad Aleskerov
Keyword(s):  
Social Decision ◽  
Decision Functions

scholarly journals A Symbolic Approach to Explaining Bayesian Network Classifiers

2018 ◽  
Author(s):  
Andy Shih ◽  
Arthur Choi ◽  
Adnan Darwiche
Keyword(s):  
Bayesian Network ◽  
Case Studies ◽  
Decision Diagrams ◽  
Minimal Set ◽  
Symbolic Form ◽  
Bayesian Network Classifiers ◽  
Current State ◽  
Current Classification ◽  
Decision Functions ◽  
Symbolic Approach

We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form. We introduce two types of explanations for why a classifier may have classified an instance positively or negatively and suggest algorithms for computing these explanations. The first type of explanation identifies a minimal set of the currently active features that is responsible for the current classification, while the second type of explanation identifies a minimal set of features whose current state (active or not) is sufficient for the classification. We consider in particular the compilation of Naive and Latent-Tree Bayesian network classifiers into Ordered Decision Diagrams (ODDs), providing a context for evaluating our proposal using case studies and experiments based on classifiers from the literature.

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