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ImageNet Challenging Classification with the Raspberry Pi: An Incremental Local Stochastic Gradient Descent Algorithm [article]

Thanh-Nghi Do
2022 arXiv   pre-print
In this paper, we propose a new incremental local stochastic gradient descent (SGD) tailored on the Raspberry Pi to deal with large ImageNet ILSVRC 2010 dataset having 1,261,405 images with 1,000 classes  ...  The local SGD splits the data block into k partitions using kmeans algorithm and then it learns in the parallel way SGD models in each data partition to classify the data locally.  ...  Acknowledgments This work has received support from the College of Information Technology, Can Tho University. We would like to thank very much the Big Data and Mobile Computing Laboratory.  ... 
arXiv:2203.11853v2 fatcat:vo2ypnwvivgxtp4b4c73bho5ti

Convolutional Neural Support Vector Machines: Hybrid Visual Pattern Classifiers for Multi-robot Systems

Jawad Nagi, Gianni A. Di Caro, Alessandro Giusti, Farrukh Nagi, Luca M. Gambardella
2012 2012 11th International Conference on Machine Learning and Applications  
We introduce Convolutional Neural Support Vector Machines (CNSVMs), a combination of two heterogeneous supervised classification techniques, Convolutional Neural Networks (CNNs) and Support Vector Machines  ...  This is the case for visual learning and recognition in multi-robot systems, where each robot acquires a different image of the same sample.  ...  The classifier is derived from the combination of wellknown classifiers for visual recognition: Support Vector Machines (SVMs) [4] and Convolutional Neural Networks (CNNs) [5] , a variant of Multi-Layer  ... 
doi:10.1109/icmla.2012.14 dblp:conf/icmla/NagiCGNG12 fatcat:sec25kn2sjh6pnopja5e663uxy

Data Mining and Soft Computing using Support Vector Machine: A Survey

Subhankar Das, Sanjib Saha
2013 International Journal of Computer Applications  
The new classification method, called Hierarchical Linear Support Vector Machine (H-LSVM), provides a very simple and efficient model in training but mainly in prediction for large-scale datasets.  ...  The new instance selection method, Multi-Class Instance Selection (MCIS), selects boundary instances as training data for substantially reducing the scale of a multi-class dataset and effectively speeds  ... 
doi:10.5120/13554-1367 fatcat:qpwndgz4qbclflfz3oardksh74

Novel RVM Approach to Structuring and Classifying Epidemic Outbreak Data

Sunaina Sharma, Veenu Mangat
2015 International Journal of Computer Applications  
Classifying is hindered by a large amount of data from various sources.  ...  The paper aims at learning kernelized RVM classifier to evaluate Ebola virus outbreak, using generalization error, intra class separability, missing probability Pi is compared to SVM.RVM relevance impact  ...  Support Vector Machine In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification  ... 
doi:10.5120/ijca2015906588 fatcat:y3xxmzytnzgklc3er3t4r7o2na

Incremental Learning Algorithm of Least Square Twin KSVC

Yaru Wang, Ling Yang, Mohan Chen, Jikui Xi, M. Kavakli, M.J.E. Salami, A. Amini, M.A.B.M. Basri, A.B. Masli, S.C.H. Li, M. Pal
2016 MATEC Web of Conferences  
on least squares twin multi-class classification support vector machine (ILST-KSVC) is proposed by solving two inverse matrix.  ...  In view of the batch implementations of standard support vector machine must be retrained from scratch every time when the training set is incremental modified, an incremental learning algorithm based  ...  It combines the standard support vector classifier and support vector regression machine together to solve the triple class directly.  ... 
doi:10.1051/matecconf/20165601005 fatcat:t3ztplq6iffbjnofbs4kwrdbsi

Big Data and Machine Learning with Hyperspectral Information in Agriculture

Kenneth Li-minn Ang, Jasmine Kah Phooi Seng
2021 IEEE Access  
INDEX TERMS Agriculture, big data, machine learning, parallel computing, hyperspectral, multispectral.  ...  The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data  ...  The authors in [3] focused on Big data and machine learning for crop protection.  ... 
doi:10.1109/access.2021.3051196 fatcat:hewivbzua5a27jotazlmqvps7i

Lung nodules detection by ensemble classification

A. Z. Kouzani, S. L. A. Lee, E. J. Hu
2008 Conference Proceedings / IEEE International Conference on Systems, Man and Cybernetics  
The performance of the developed method is compared against that of the support vector machine and the decision tree methods.  ...  Three experiments are performed using lung scans of 32 patients including thousands of images within which nodule locations are marked by expert radiologists.  ...  ACKNOWLEDGMENT The support of the Victorian Partnership for Advanced Computing (VPAC) under an e-Research Program Grants Scheme is gratefully acknowledged.  ... 
doi:10.1109/icsmc.2008.4811296 dblp:conf/smc/KouzaniLH08 fatcat:h7xdklyvwveyfnvl3lxm3r7dmq

Online feature extraction for the incremental learning of gestures in human-swarm interaction

Jawad Nagi, Alessandro Giusti, Farrukh Nagi, Luca M. Gambardella, Gianni A. Di Caro
2014 2014 IEEE International Conference on Robotics and Automation (ICRA)  
We present a novel approach for the online learning of hand gestures in swarm robotic (multi-robot) systems.  ...  To learn and classify gestures in an online and incremental fashion, we employ a 2nd order online learning method, namely the Soft-Confidence Weighted (SCW) learning scheme.  ...  ACKNOWLEDGMENTS This research was supported by the Swiss National Science Foundation (SNSF) through the National Centre of Competence in Research (NCCR) Robotics (www.nccr-robotics.ch).  ... 
doi:10.1109/icra.2014.6907338 dblp:conf/icra/NagiGNGC14 fatcat:7menzlmu6zh4rc5ckhvahdga7m

Multi-label Learning Based on Kernel Extreme Learning Machine

Fangfang Luo, Wenzhong Guo, Fangwan Huang, Guolong Chen
2018 DEStech Transactions on Computer Science and Engineering  
In recent years, with the increase of data scale, multi-label learning with large scale class labels has turned out to be the research hotspots.  ...  In particular, in terms of large matrix inverse problem, the large matrix is divided into small matrices for parallel computation through using matrix block method.  ...  Extreme learning machine (ELM) has been applied to multi-label learning with small scale datasets, and good results have been obtained [1] .  ... 
doi:10.12783/dtcse/csae2017/17476 fatcat:362vdxzye5bcrbq2ddw5nnab2i

Program

2009 2009 International Joint Conference on Neural Networks  
Wang and Jinglu Hu P224 SVM+ Regression and Multi-Task Learning Feng Cai and Vladimir Cherkassky P225 GNG-SVM Framework -Classifying Large Datasets With Support Vector Machines Using Growing Neural Gas  ...  Ondrej Linda and Milos Manic P226 Help-Training semi-supervised LS-SVM Mathias Adankon and Mohamed Cheriet P227 A Support Vector Hierarchical Method for Multi-class Classification and Rejection Yu-Chiang  ... 
doi:10.1109/ijcnn.2009.5178575 fatcat:kxaceopferd23ps5uyrn3m7xjy

Classification of quickbird image with maximal mutual information feature selection and support vector machine

Bo Wu, Zhu-guo Xiong, Yun-zhi Chen, Yin-di Zhao
2009 Procedia Earth and Planetary Science  
, and 3) support vector machine for classification.  ...  It also demonstrates that the proposed maximal mutual information feature selection with support vector machine classifier significantly outperforms the classification method accompanied with eCoginition  ...  Support vector machine classifier Support vector machine (SVM) is used for the purpose of evaluating the proposed feature selection method and classification.  ... 
doi:10.1016/j.proeps.2009.09.179 fatcat:aks44ykj2nddpi4lkjtgmieg3q

Scene Oriented Classification of Blurry and Noisy Images Using SVM with Fuzzy C Mean Clustering

Deepak Nema
2013 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY  
To overcome this problem, we propose a combination of Support Vector Machine (SVM) and Fuzzy C-mean.  ...  In this work, we present an extension of scene oriented hierarchical classification of blurry and noisy images using Support Vector Machine (SVM) and Fuzzy C-Mean.  ...  The proposed approach is the combination of Support Vector Machine (SVM) and Fuzzy C Mean (FCM) clustering for final classification.  ... 
doi:10.24297/ijct.v12i4.3186 fatcat:dlgahojsyfbvpfrz4jph4di5i4

Dynamic classification for video stream using support vector machine

Mariette Awad, Yuichi Motai
2008 Applied Soft Computing  
The authors would like to thank IBM Microelectronics (Essex Junction, Vermont) for the support and time used in this study.  ...  Acknowledgement The authors acknowledge Jane Brooks Zurn and Xianhua Jiang for their comments.  ...  - A dynamic classification using the support vector machine (SVM) technique is presented in this paper as a new 'incremental' framework for multiple-classifying video stream data.  ... 
doi:10.1016/j.asoc.2007.11.008 fatcat:scrzs67ezzfubawnpx6n6rwonu

Comprehensive Review On Twin Support Vector Machines [article]

M. Tanveer and T. Rajani and R. Rastogi and Y.H. Shao and M. A. Ganaie
2021 arXiv   pre-print
Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression  ...  It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TWSVM and requires  ...  This scaled the model to large scale data. Twin Support Vector Machine for Universum Data and Imbalanced Datasets In 2012, Qi et al.  ... 
arXiv:2105.00336v2 fatcat:prxup4sbavfyxpembij6amrnka

Machine Learning and Integrative Analysis of Biomedical Big Data

Bilal Mirza, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, Peipei Ping
2019 Genes  
data, class imbalance and scalability issues.  ...  ., genome) is analyzed in isolation using statistical and machine learning (ML) methods.  ...  Online machine learning algorithms including online sequential extreme learning machine (OS-ELM), incremental decremental support vector machine (IDSVM), and online deep learning are attractive for big  ... 
doi:10.3390/genes10020087 pmid:30696086 pmcid:PMC6410075 fatcat:vopnjgke4fculmr7t3n43ewfiy
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