scholarly journals Handwritten Arabic Numeral Character Recognition Using Multi Kernel Support Vector Machine

2019 ◽  
pp. 99-106
Author(s):  
Muhammad Athoillah ◽  
Rani Kurnia Putri
Keyword(s):  
Support Vector Machine ◽  
Mother Tongue ◽  
Arabic Numeral ◽  
Classification Problem ◽  
Kernel Learning ◽  
Support Vector ◽  
Learning Method ◽  
Space Data ◽  
Handwritten Recognition ◽  
Handwritten Arabic

 Handwritten recognition is how computer can identify a handwritten character or letter from a document, an image or another source. Recently, many devices provide a feature using handwritten as an input such as laptops, smartphones, and others, affecting handwritten recognition abilities become important thing. As the mother tongue of Muslims, and the only language used in holy book Al Qur’an, therefore recognizing in arabic character is a challenging task. The outcome of that recognizing system has to be quite accurate, the results of the process will impact on the entire process of understanding the Qur’an lesson. Basically handwritten recognition problem is part of classification problem and one of the best algorithm to solve it is Support Vector Machine (SVM). By finding a best separate line and two other support lines between input space data in process of training, SVM can provide the better result than other classify algorithm. Although SVM can solve the classify problem well, SVM must be modified with kernel learning method to be able to classify nonlinear data. However, determining the best kernel for every classification problem is quite difficult. Therefore, some technique have been developed, one of them is Multi Kernel Learning (MKL). This technique works by combining some kernel function to be one kernel with an equation. This framework built an application to recognize handwritten arabic numeral character using SVM algorithm that modified with Kernel Learning Method. The result shows that the application can recognize data well with average value of Accuracy is 84,37%

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

Detecting Edge Using Support Vector Machine

2012 ◽  
Vol 588-589 ◽  
pp. 974-977 ◽  
Author(s):  
Jih Pin Yeh
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Edge Detection ◽  
Supervised Learning ◽  
Classical Method ◽  
Support Vector ◽  
Learning Method ◽  
Edge Detector

The edge detection is used in many applications in image processing. It is currently crucial technique of image processing. There are various methods for promoting edge detection. Here, it is presented that edge detection can be achieved using Support Vector Machine (SVM). Supervised learning method is applied. Laplacian edge detector is an instructor of Support Vector Machine. In this research, it is presented that any classical method can be applied for training of SVM as edge detector.

Incremental Regressive Learning Algorithm of Support Vector Machine and its Application

2011 ◽  
Vol 216 ◽  
pp. 301-306
Author(s):  
Shi Hua Zhang ◽  
Xi Long Qu ◽  
Xue Ye Wang
Keyword(s):  
Support Vector Machine ◽  
Transition Temperature ◽  
Incremental Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Learning Ability ◽  
Support Vector ◽  
Learning Method ◽  
Essential Characteristic ◽  
Incremental Learning Method

There is no incremental learning ability for the traditional support vector machine (SVM) and there are all kind of merits and flaws for usually used incremental learning method. Normal SVM is unable to train in large-scale samples, while the computer’s memory is limited. In order to resolve this problem and improve training speed of the SVM, we analyze essential characteristic of SVM and bring up the incremental learning algorithm of SVM based on regression of SVM related to SV (support vectors). The algorithm increases the speed of training and can be able to learning with large-scale samples while its regressive precision loses fewer. The experiments show that SVM performs effectively and practically. Its application to prediction of the transition temperature (Tg) for high molecular polymers show that this model (R2=0.9427) proved to be considerably more accurate compared to a ANNs regression model (R2=0.9269).

A Multi-Classification Method Based on Gaussian Processes

2012 ◽  
Vol 198-199 ◽  
pp. 1333-1337 ◽  
Author(s):  
San Xi Wei ◽  
Zong Hai Sun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Good Accuracy ◽  
Classification Problem ◽  
Decision Time ◽  
Support Vector ◽  
Regression Problem ◽  
Multi Classification

Gaussian processes (GPs) is a very promising technology that has been applied both in the regression problem and the classification problem. In recent years, models based on Gaussian process priors have attracted much attention in the machine learning. Binary (or two-class, C=2) classification using Gaussian process is a very well-developed method. In this paper, a Multi-classification (C>2) method is illustrated, which is based on Binary GPs classification. A good accuracy can be obtained through this method. Meanwhile, a comparison about decision time and accuracy between this method and Support Vector Machine (SVM) is made during the experiments.

scholarly journals Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

2012 ◽  
Vol 60 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Hamid Shahraiyni ◽  
Mohammad Ghafouri ◽  
Saeed Shouraki ◽  
Bahram Saghafian ◽  
Mohsen Nasseri
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Support Vector ◽  
Learning Method ◽  
Runoff Simulation ◽  
Karoon River ◽  
Discharge Data ◽  
Daily Streamflow ◽  
Runoff Modeling ◽  
Active Learning Method

Comparison Between Active Learning Method and Support Vector Machine for Runoff ModelingIn this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m3s-1, 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m3s-1, 0.81, 7.3%, and -3.6%, respectively. The results show appropriate and acceptable simulation by ALM and optimized SVM. Optimized SVM is a well-known method for runoff simulation and its capabilities have been demonstrated. Therefore, the similarity between ALM and optimized SVM results imply the ability of ALM for runoff modeling. In addition, ALM training is easier and more straightforward than the training of many other data driven models such as optimized SVM and it is able to identify and rank the effective input variables for the runoff modeling. According to the results of ALM simulation and its abilities and properties, it has merit to be introduced as a new modeling method for the runoff modeling.

scholarly journals A New Support Vector Machine Based on Convolution Product

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Wei-Chang Yeh ◽  
Yunzhi Jiang ◽  
Shi-Yi Tan ◽  
Chih-Yen Yeh
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Numerical Experiments ◽  
Classification Problem ◽  
Support Vector ◽  
Convolution Product ◽  
Swarm Optimization ◽  
New Information ◽  
Short Time

The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. This paper proposes a novel convolutional SVM (CSVM) that has the advantages of both CNN and SVM to improve the accuracy and effectiveness of mining smaller datasets. The proposed CSVM adapts the convolution product from CNN to learn new information hidden deeply in the datasets. In addition, it uses a modified simplified swarm optimization (SSO) to help train the CSVM to update classifiers, and then the traditional SVM is implemented as the fitness for the SSO to estimate the accuracy. To evaluate the performance of the proposed CSVM, experiments were conducted to test five well-known benchmark databases for the classification problem. Numerical experiments compared favorably with those obtained using SVM, 3-layer artificial NN (ANN), and 4-layer ANN. The results of these experiments verify that the proposed CSVM with the proposed SSO can effectively increase classification accuracy.

scholarly journals Hierarchical Multi Twin Support Vector Machine

2021 ◽  
Vol 130 (2B) ◽  
Author(s):  
Nguyen The Cuong
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Data Type ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Better Than

In binary classification problems, two classes normally have different tendencies. More complex, the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) don't sufficiently exploit structural information with cluster granularity of the data, cause of restricts the capability of simulation of data trends. Structural twin support vector machine (S-TWSVM) sufficiently exploits structural information with cluster granularity of one class for learning a represented hyperplane of that class. This makes S-TWSVM's data simulation capabilities better than TWSVM. However, for the data type that each class consists of clusters of different trends, the capability of simulation of S-TWSVM is restricted. In this paper, we propose a new Hierarchical Multi Twin Support Vector Machine (called HM-TWSVM) for classification problem with each cluster-vs-class strategy. HM-TWSVM overcomes the limitations of S-TWSVM. Experiment results show that HM-TWSVM could describe the tendency of each cluster.

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

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