An Extreme Learning Machine and Action Recognition Algorithm for Generalized Maximum Clique Problem in Video Event Recognition

2021 ◽  
Vol 30 (8) ◽  
Author(s):  
R Kavitha ◽  
D Chitra
Keyword(s):  
Extreme Learning Machine ◽  
Action Recognition ◽  
Maximum Clique ◽  
Recognition Algorithm ◽  
Event Recognition ◽  
Maximum Clique Problem ◽  
Video Event ◽  
Learning Machine ◽  
Clique Problem

An effective local search for the maximum clique problem

2005 ◽  
Vol 95 (5) ◽  
pp. 503-511 ◽  
Author(s):  
Kengo Katayama ◽  
Akihiro Hamamoto ◽  
Hiroyuki Narihisa
Keyword(s):  
Local Search ◽  
Maximum Clique ◽  
Maximum Clique Problem ◽  
Clique Problem

A novel computing model of the maximum clique problem based on circular DNA

2010 ◽  
Vol 53 (7) ◽  
pp. 1409-1416 ◽  
Author(s):  
Jing Yang ◽  
Cheng Zhang ◽  
Jin Xu ◽  
XiangRong Liu ◽  
XiaoLi Qiang
Keyword(s):  
Maximum Clique ◽  
Maximum Clique Problem ◽  
Circular Dna ◽  
Computing Model ◽  
Clique Problem

scholarly journals Randomized heuristic for the maximum clique problem

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Approximation Algorithm ◽  
Randomized Algorithm ◽  
Maximum Clique ◽  
Constant Factor ◽  
Maximum Clique Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Np Hard Problem ◽  
Java Code ◽  
Clique Problem

<p>A clique in a graph is a set of vertices that are all directly connected</p><p>to each other i.e. a complete sub-graph. A clique of the largest size is</p><p>called a maximum clique. Finding the maximum clique in a graph is an</p><p>NP-hard problem and it cannot be solved by an approximation algorithm</p><p>that returns a solution within a constant factor of the optimum. In this</p><p>work, we present a simple and very fast randomized algorithm for the</p><p>maximum clique problem. We also provide Java code of the algorithm</p><p>in our git repository. Results show that the algorithm is able to find</p><p>reasonably good solutions to some randomly chosen DIMACS benchmark</p><p>graphs. Rather than aiming for optimality, we aim to find good solutions</p><p>very fast.</p>

scholarly journals Randomized heuristic for the maximum clique problem

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Approximation Algorithm ◽  
Randomized Algorithm ◽  
Maximum Clique ◽  
Constant Factor ◽  
Maximum Clique Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Np Hard Problem ◽  
Java Code ◽  
Clique Problem

<p>A clique in a graph is a set of vertices that are all directly connected</p><p>to each other i.e. a complete sub-graph. A clique of the largest size is</p><p>called a maximum clique. Finding the maximum clique in a graph is an</p><p>NP-hard problem and it cannot be solved by an approximation algorithm</p><p>that returns a solution within a constant factor of the optimum. In this</p><p>work, we present a simple and very fast randomized algorithm for the</p><p>maximum clique problem. We also provide Java code of the algorithm</p><p>in our git repository. Results show that the algorithm is able to find</p><p>reasonably good solutions to some randomly chosen DIMACS benchmark</p><p>graphs. Rather than aiming for optimality, we aim to find good solutions</p><p>very fast.</p>

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