A Survey of Spiking Neural Networks and Support Vector Machine Performance Byusinggpu's

Israel Tabarez-Paz1, Neil Hernandez-Gress2 and Miguel Gonzalez Mendoza2, 1Universidad Autonoma del Estado de Mexico, Mexico and 2Campus Estado de Mexico, Mexico

ABSTRACT

In this paper we study the performance of Spiking Neural Networks (SNN)and Support Vector Machine (SVM) by using a GPU, model GeForce 6400M. Respect to applications of SNN, the methodology may be used for clustering, classification of databases, odor, speech and image recognition..In case of methodology SVM, is typically applied for clustering, regression and progression. According to particular characteristics of these methodologies,theycan be parallelizedin several grades. However, level of parallelism is limited to architecture of hardware. So, is very sure to get better results using other hardware with more computational resources. The different approaches are evaluated by the training speed and performance. On the other hand, some authors have coded algorithms SVM light, but nobody has programming QP SVM in a GPU. Algorithms were coded by authors in the hardware, like Nvidia card, FPGA or sequential circuits that depends on methodology used, to compare learning timewith between GPU and CPU. Also, in the survey we introduce a brief description of the types of ANN and its techniques of execution to be related with results of researching.

KEYWORDS

GPU, Spiking Neural Networks, Support Vector Machines, pattern recognition.

Original Source URL: http://airccse.org/journal/ijsc/papers/4313ijsc01.pdf

https://airccse.org/journal/ijsc/current2013.html

#Fuzzysystems #Softcomputing #Neuralnetworks #Wavelet #Patternrecognition #Machinelearning

Methodological Study Of Opinion Mining And Sentiment Analysis Techniques

Pravesh Kumar Singh and Mohd Shahid Husain, Integral University, India

ABSTRACT

Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis. 

KEYWORDS

Opinion Mining, Sentiment Analysis, Feature Extraction Techniques, Naïve Bayes Classifiers, Clustering, Support Vector Machines

Original Source URL: https://airccse.org/journal/ijsc/papers/5114ijsc02.pdf

https://airccse.org/journal/ijsc/current2014.html

A SURVEY OF SPIKING NEURAL NETWORKS AND SUPPORT VECTOR MACHINE PERFORMANCE BYUSINGGPU’S

A SURVEY OF SPIKING NEURAL NETWORKS AND SUPPORT VECTOR MACHINE PERFORMANCE BYUSINGGPU’S 

Israel Tabarez-Paz1 , Neil Hernández-Gress2 and Miguel González Mendoza2 . 1Universidad Autónomadel Estado de México Blvd. Universitario s/n, Predio San Javier Atizapán de Zaragoza, México  2 Tecnológico de Monterrey, Campus Estado de México, CarreteraLago de Guadalupe km 3.5 Atizapán de Zaragoza Col. Margarita Maza de Juarez, Atizapán de Zaragora, México

ABSTRACT 

In this paper we study the performance of Spiking Neural Networks (SNN)and Support Vector Machine (SVM) by using a GPU, model GeForce 6400M. Respect to applications of SNN, the methodology may be used for clustering, classification of databases, odor, speech and image recognition..In case of methodology SVM, is typically applied for clustering, regression and progression. According to particular characteristics of these methodologies,theycan be parallelizedin several grades. However, level of parallelism is limited to architecture of hardware. So, is very sure to get better results using other hardware with more computational resources. The different approaches are evaluated by the training speed and performance. On the other hand, some authors have coded algorithms SVM light, but nobody has programming QP SVM in a GPU. Algorithms were coded by authors in the hardware, like Nvidia card, FPGA or sequential circuits that depends on methodology used, to compare learning timewith between GPU and CPU. Also, in the survey we introduce a brief description of the types of ANN and its techniques of execution to be related with results of researching. 

KEYWORDS 

GPU,Spiking Neural Networks, Support Vector Machines, pattern recognition.  

Original Source URL : http://airccse.org/journal/ijsc/papers/4313ijsc01.pdf

http://airccse.org/journal/ijsc/current2013.html