Touch-based Smartphone Authentication Using Import Vector Domain Description

2018 ◽  
Author(s):  
Bin Zou ◽  
Yantao Li
Keyword(s):  
Domain Description

scholarly journals Substructure Discovery Using Minimum Description Length and Background Knowledge

1994 ◽  
Vol 1 ◽  
pp. 231-255 ◽  
Author(s):  
D. J. Cook ◽  
L. B. Holder
Keyword(s):  
Minimum Description Length ◽  
Background Knowledge ◽  
Structural Data ◽  
Original Data ◽  
Minimum Description Length Principle ◽  
Online Appendix ◽  
Discovery System ◽  
Graph Match ◽  
Domain Description ◽  
Graph Form

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.

Ontology-Driven Situation Assessment System Design and Development in IoT Domains

2020 ◽  
pp. 1465-1483
Author(s):  
Sergey Lebedev ◽  
Michail Panteleyev
Keyword(s):  
Traffic Control ◽  
External Environment ◽  
Main Idea ◽  
Assessment System ◽  
Design And Development ◽  
Situation Assessment ◽  
Process Ontology ◽  
Situational Model ◽  
Domain Description ◽  
Domain Independent

An ontology-driven approach to software design and development of situation assessment systems (SAS) for IoT applications is considered. As SAS is used to build the situational model for the external environment, it highly depends on the operational domain. To simplify the transition from the domain description to SAS dataflow process the ontology-driven approach is proposed. The main idea of the approach is to explicitly formalize SAS dataflow process in an ontological form. For this purpose, a domain-independent SAS ontology is proposed that allows automation of the dataflow process design. The dataflow process ontology is used to automate development and runtime stages of SAS lifecycle. The proposed ontology is included into the proposed instrument set. The set can be used to build SAS systems for different domains described with OWL ontology. The set is evaluated on a traffic control scenario.

Support vector domain description

1999 ◽  
Vol 20 (11-13) ◽  
pp. 1191-1199 ◽  
Author(s):  
David M.J Tax ◽  
Robert P.W Duin
Keyword(s):  
Support Vector ◽  
Domain Description

Constraint Management of Reduced Representation Variables in Decomposition-Based Design Optimization

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Michael J. Alexander ◽  
James T. Allison ◽  
Panos Y. Papalambros ◽  
David J. Gorsich
Keyword(s):  
Design Optimization ◽  
Support Vector ◽  
Management Technique ◽  
Efficient Design ◽  
Reduced Representation ◽  
Constraint Management ◽  
Target Cascading ◽  
Simple Bounds ◽  
Domain Description ◽  
Reduced Representations

In decomposition-based design optimization strategies such as analytical target cascading (ATC), it is sometimes necessary to use reduced representations of highly discretized functional data exchanged among subproblems to enable efficient design optimization. However, the variables used by such reduced representation methods are often abstract, making it difficult to constrain them directly beyond simple bounds. This problem is usually addressed by implementing a penalty value-based heuristic that indirectly constrains the reduced representation variables. Although this approach is effective, it leads to many ATC iterations, which in turn yields an ill-conditioned optimization problem and an extensive runtime. To address these issues, this paper introduces a direct constraint management technique that augments the penalty value-based heuristic with constraints generated by support vector domain description (SVDD). A comparative ATC study between the existing and proposed constraint management methods involving electric vehicle design indicates that the SVDD augmentation is the most appropriate within decomposition-based design optimization.

Sign in / Sign up

Export Citation Format

Share Document