Using Directed Acyclic Graph Support Vector Machines with Tabu Search for Classifying Faulty Product Types

2006 ◽  
pp. 1117-1125
Author(s):  
Ping-Feng Pai ◽  
Yu-Ying Huang
Keyword(s):  
Support Vector Machines ◽  
Tabu Search ◽  
Directed Acyclic Graph ◽  
Support Vector ◽  
Acyclic Graph ◽  
Vector Machines

scholarly journals Jumlah Transisi pada Ciri Transisi dalam Pengenalan Pola Tulisan Tangan Aksara Jawa Nglegeno dengan Multiclass Support Vector Machines

2012 ◽  
Vol 8 (1) ◽  
pp. 18
Author(s):  
Azis Wisnu Widhi Nugraha ◽  
Widhiatmoko Hery Purnomo
Keyword(s):  
Support Vector Machines ◽  
Directed Acyclic Graph ◽  
Recognition System ◽  
Support Vector ◽  
Paper Report ◽  
Acyclic Graph ◽  
System Transition ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Multiclass Support Vector Machines

<p>Feature extraction is one of the most improtant step on characters recognition system. Transition features is one from many features used on characters recognition system. This paper report a research on handwritten basic Jawanesse characters recognition system to found the proper numbers of transitions used on transition features. To recognize the characters,the Multiclass Support Vector Machines were used. The Directed Acyclic Graph (DAG) SVM were used for multiclass classification strategy and to map each input vector to a higher dimention space, the Gaussian Radial Basis Function (RBF) kernel with parameter 1were used. It can be shown, for basicJawanesse characters recognition system, the optimal numbers of transitions used for transition features is 4 (a half of maximum numbers of transition on all patterns).</p>

An Improved Directed Acyclic Graph Support Vector Machines Based on Inter-Cluster Separability Measure in Feature Space

2012 ◽  
Vol 229-231 ◽  
pp. 2002-2006
Author(s):  
Qing Ai ◽  
Ji Zhao ◽  
Yu Ping Qin
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Directed Acyclic Graph ◽  
Feature Space ◽  
Experimental Results ◽  
Support Vector ◽  
Acyclic Graph ◽  
Vector Machines ◽  
The Matrix ◽  
Cluster Separability

For disadvantage of the methods that are used to evaluate inter-cluster separability measure, a novel separability measure is proposed and applied to directed acyclic graph support vector machine. The distance between cluster centers and distribution of samples in feature space are both considered by the algorithm. Firstly, use hyper-sphere support vector machine to obtain minimal bounding hyper-sphere of each cluster, according to the radius and centers of minimal bounding hyper-spheres, introduce the concept of inter-cluster separability measure in feature space, get the matrix of inter-cluster separability measure according to the concept, finally construct the directed acyclic graph according to the matrix. The experimental results show that the algorithm has higher classification precision, comparing with old directed acyclic graph support vector machine.

scholarly journals TABU SEARCH MODEL SELECTION FOR SVM

2008 ◽  
Vol 18 (01) ◽  
pp. 19-31 ◽  
Author(s):  
GILLES LEBRUN ◽  
CHRISTOPHE CHARRIER ◽  
OLIVIER LEZORAY ◽  
HUBERT CARDOT
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Tabu Search ◽  
Recognition Rate ◽  
Decision Function ◽  
Support Vector ◽  
Feature Subset ◽  
Binary Decision ◽  
Vector Machines ◽  
Decision Functions

A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.

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