Line Segmentation Approach for Ancient Palm Leaf Manuscripts Using Competitive Learning Algorithm

2016 ◽  
Author(s):  
Dona Valy ◽  
Michel Verleysen ◽  
Kimheng Sok
Keyword(s):  
Learning Algorithm ◽  
Competitive Learning ◽  
Segmentation Approach ◽  
Line Segmentation ◽  
Palm Leaf

Reduction Models in Competitive Learning Founded on Distortion Standards

2008 ◽  
Vol 12 (3) ◽  
pp. 314-323 ◽  
Author(s):  
Michiharu Maeda ◽  
◽  
Noritaka Shigei ◽  
Hiromi Miyajima ◽  
Kenichi Suzaki ◽  
...  
Keyword(s):  
Image Coding ◽  
Learning Algorithm ◽  
Computation Time ◽  
Competitive Learning ◽  
Experimental Results ◽  
Error Criterion ◽  
General Reduction ◽  
Predetermined Number

Two reductions in competitive learning founded on distortion standards are discussed from the viewpoint of generating necessary and appropriate reference vectors under the condition of their predetermined number. The first approach is termed the segmental reduction and competitive learning algorithm. The algorithm is presented as follows: First, numerous reference vectors are prepared and the algorithm is processed under competitive learning. Next, reference vectors are sequentially eliminated to reach their prespecified number based on the partition error criterion. The second approach is termed the general reduction and competitive learning algorithm. The algorithm is presented as follows: First, numerous reference vectors are prepared and the algorithm is processed under competitive learning. Next, reference vectors are sequentially erased based on the average distortion criterion. Experimental results demonstrate the effectiveness of our approaches compared to conventional techniques in average distortion. The two approaches are applied to image coding to determine their feasibility in quality and computation time.

COMPENSATION COMPETITIVE LEARNING

2001 ◽  
Vol 01 (03) ◽  
pp. 303-322
Author(s):  
ZHI-QIANG LIU ◽  
YAJUN ZHANG
Keyword(s):  
Input Data ◽  
Learning Algorithm ◽  
A Priori ◽  
Competitive Learning ◽  
Experimental Results ◽  
Initial Number ◽  
Number Of Clusters ◽  
And Performance ◽  
Competitive Algorithms

In general, in competitive learning the requirement for the initial number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. The behavior and performance of the competitive algorithms are very sensitive to the initial locations and number of the prototypes. In this paper after investigating several important competitive learning paradigms, we present compensation techniques for overcoming the problems in competitive learning. Our experimental results show that competition with compensation can improve the performance of the learning algorithm.

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