SVM with OpenCL: High performance implementation of support vector machines on heterogeneous systems

2015 ◽  
Author(s):  
Ethan Peters ◽  
Andreas Savakis
Keyword(s):  
Support Vector Machines ◽  
High Performance ◽  
Heterogeneous Systems ◽  
Support Vector ◽  
Vector Machines

scholarly journals INPUTING STUDENTS’ SCORE BASED ON GIST FEATURES, SUPPORT VECTOR MACHINES AND TESSERACT

2020 ◽  
Vol 1 (41) ◽  
pp. 77-85
Author(s):  
Hau Hung Nguyen
Keyword(s):  
Support Vector Machines ◽  
High Performance ◽  
Regions Of Interest ◽  
Experimental Results ◽  
Support Vector ◽  
Vocational College ◽  
Vector Machines ◽  
Handwritten Digit

Handwriting recogination plays an important role in data inputing and processing in the practice. This attracts much attention of many researchers in different fields. In this paper, a new algorithm is proposed by basing on GIST features, Support Vector Machines (SVM) and Tesseract for entering the score on students’ transcript form at Soc Trang Vocational College. The algorithm consists of two main works, i.e., recognizing students’code and recogziing handwritten digit. In the proposed algorithm, all regions of interest are determined and extract their dictint features with using tesseract and GIST. Then, these features are classified by SVM mechanism. Experimental results demonstrated that the proposed algorithm obtained high performance with accuracy up to 96,57% for students’ code and 93,55% for Handwritting scores. Average time was 7,9s per one transcript.

Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines

2018 ◽  
Author(s):  
Nelson Marcelo Romero Aquino ◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Heitor Silvério Lopes
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Soft Biometrics ◽  
Vector Machines
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