A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications (original) (raw)

• Smeulders A, Worring M, Santini S, Gupta A, Jain R (2000) Content based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380
  Article Google Scholar
• Tsai C, Hung C (2008) Automatically annotating images with keywords: a review of image annotation systems. Recent Pat Comput Sci 1:55–68
  Article Google Scholar
• Wong W, Hsu S (2006) Application of SVM and ANN for image retrieval. Eur J Oper Res 173(3):938–950
  Article MathSciNet MATH Google Scholar
• Gao Y, Fan J (2005) Semantic image classification with hierarchical feature subset selection. In: Proceedings of the ACM multimedia workshop on multimedia information retrieval, pp 135–142
• Boutell M, Luo J, Shen X, Brown M (2004) Learning multi-label scene classification. Pattern Recognit 37(9):1757–1771
  Article Google Scholar
• Chen Y, Wang JZ (2004) Image categorization by learning and reasoning with regions. J Mach Learn Res 5:913–939
  MathSciNet Google Scholar
• Cusano C, Ciocca G, Schettini R (2004) Image annotation using SVM. In: Proceedings of SPIE conference on internet imaging, pp 330–338
• Fan J, Gao Y, Luo H, Xu G (2004) Automatic image annotation by using concept-sensitive salient objects for image content representation. In: Proceedings of the 27th annual international conference on research and development in information retrieval (SIGIR), pp 361–368
• Le Saux B, Amato G (2004) Image recognition for digital libraries. In: Proceedings of the ACM multimedia workshop on multimedia information retrieval (MIR), pp 91–98
• Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
  Book MATH Google Scholar
• Abe S (2005) Support vector machines for pattern classification (advances in pattern recognition. Springer, New York
  Google Scholar
• Waring C, Liu X (2005) Face detection using spectral histograms and SVMs. IEEE Trans Syst Man Cybern Part B 35(3):467–476
  Article Google Scholar
• Colas F, Brazdil P (2006) Comparison of SVM and some older classification algorithms in text classification tasks. In: Proceedings of IFIP-AI world computer congress, pp 169–178
• Do T, Nguyen V, Poulet F (2008) Speed up SVM algorithm for massive classification tasks. In: Proceedings of the 4th international conference on advanced data mining and applications (ADMA), pp 147–157
• Alham NK, Li M, Hammoud S, Qi H (2009) Evaluating machine learning techniques for automatic image annotations. In: Proceedings of the 6th international conference on fuzzy systems and knowledge discovery (FSKD), pp 245–249
• Hazan T, Man A, Shashua A (2008) A parallel decomposition solver for SVM: distributed dual ascend using fenchel duality. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8
• Zanghirati G, Zanni L (2003) A parallel solver for large quadratic programs in training support vector machines. Parallel Comput 29(4):535–551
  Article MathSciNet MATH Google Scholar
• Do T, Poulet F (2006) Classifying one billion data with a new distributed SVM algorithm. In: Proceedings of the international conference on research, innovation and vision for the future (RIVF), pp 59–66
• Chang EY, Zhu K, Wang H, Bai H, Li J, Qiu Z (2007) PSVM: parallelizing support vector machines on distributed computers. In: Proceedings of advances in neural information processing systems, pp 257–264
• Zhao HX, Magoules F (2011) Parallel support vector machines on multi-core and multiprocessor systems. Proceedings of the 11th International Conference on Artificial Intelligence and Applications (AIA 2011)
• Zhu-Hong Y, Jian-Zhong Y, Lin Z, Shuai L, Zhen-Kun W (2014) A MapReduce based parallel SVM for large-scale predicting protein-protein interactions. Neurocomputing 145:37–43
  Article Google Scholar
• Cao L, Keerthi S, Chong-Jin O, Zhang J, Periyathamby U, Ju FuX, Lee H (2006) Parallel sequential minimal optimization for the training of support vector machines. IEEE Trans Neural Netw 17(4):1039–1049
  Article Google Scholar
• Bickson D, Yom-Tov E, Dolev D (2008) A Gaussian belief propagation solver for large scale support vector machines. In: Proceedings of the 5th European conference on complex systems
• Woodsend K, Gondzio J (2009) Hybrid MPI/OpenMP parallel linear support vector machine training. J Mach Learn Res 10:1937–1953
  MathSciNet MATH Google Scholar
• Bottou L, Bordes A, Ertekin S (2012) LASVM. http://leon.bottou.org/projects/lasvm. Accessed 19 May 2015
• Cao LJ, Keerthi SS, Ong CJ, Uvaraj P, Fu XJ, Lee HP (2006) Developing parallel sequential minimal optimization for fast training support vector machine. Neurocomputing 70(1–3):93–104
  Article Google Scholar
• Khalid Alham N (2011) Parallelizing support vector machines for scalable image annotation. PhD Thesis, Brunel University
• Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical report, MSR-TR-98-14, Microsoft Research
• Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
  Article Google Scholar
• Apache Hadoop!. http://hadoop.apache.org. Accessed 19 May 2015
• Venner J (2009) Programming Hadoop. Springer, Berlin, pp 1–407
  Book Google Scholar
• Graf H, Cosatto E, Bottou L, Durdanovic I, Vapnik V (2004) Parallel support vector machines: the cascade SVM. In: Proceedings of advances in neural information processing systems (NIPS)
• Yang J (2006) An improved cascade SVM training algorithm with crossed feedbacks. In: Proceedings of 1st international multi-symposium of computer and computational sciences (IMSCCS), pp 735–738
• Ming-Hsuan Y, Moghaddam B (2000) Support vector machines for visual gender classification. In: Proceedings of the international conference on pattern recognition (ICOR), pp 5115–5118
• Osuna E, Freund R, Girosit F (1997) Training support vector machines: an application to face detection. In: Proceedings of computer vision and pattern recognition (CVPR), pp 130–136
• Bach FR, Jordan MI (2005) Predictive low-rank decomposition for kernel methods. In: Proceedings of the 22nd international conference on machine learning (ICML), pp 33–40
• Suykens JK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
  Article MathSciNet MATH Google Scholar
• Collobert R, Bengio S, Bengio Y (2002) A parallel mixture of SVMs for very large scale problems. Neural Comput 14(5):1105–1114
  Article MATH Google Scholar
• Huang G, Mao K, Siew C, Huang D (2005) Fast modular network implementation for support vector machines. IEEE Trans Neural Netw 16(6):1651–1663
  Article Google Scholar
• Lu Y, Roychowdhury V, Vandenberghe L (2008) Distributed parallel support vector machines in strongly connected networks. IEEE Trans Neural Netw 19(7):1167–1178
  Article Google Scholar
• Kun D, Yih L, Perera A (2003) Parallel SMO for training support vector machines, SMA 5505, project final report
• The Cilk Project. http://supertech.csail.mit.edu/cilk/. Accessed 19 May 2015
• Kuszmaul BC (2007) Cilk provides the “best overall productivity” for high performance computing: (and won the HPC challenge award to prove it). In: Proceedings of the 9th ACM symposium on parallel algorithms and architectures (SPAA), pp 299–300
• Catanzaro B, Sundaram N, Keutzer K (2008) Fast support vector machine training and classification on graphics processors. In: Proceedings of the 25th international conference on machine learning (ICML), pp 104—111
• Dong J, Krzyżak A, Suen C (2003) A fast parallel optimization for training support vector machine. In: Proceedings of the 3rd international conference on machine learning and data mining in pattern recognition (MLDM), pp 96–105
• Winters-Hilt S, Armond K (2012) Distributed SVM learning and support vector reduction. http://cs.uno.edu/~winters/SVM_SWH_preprint.pdf. Accessed 19 May 2015
• Gillick D, Faria A, DeNero J (2012) MapReduce: distributed computing for machine learning. http://www.icsi.berkeley.edu/~arlo/publications/gillick_cs262a_proj.pdf. Accessed 19 May 2015
• Chu C, Kim S, Lin Y, Yu Y, Bradski G, Ng A, Olukotun K (2006) Map-reduce for machine learning on multicore. In: Schölkopf B, Platt JC, Hoffman T (eds) NIPS. MIT Press, Cambridge, pp 281–288
  Google Scholar
• Sun Z, Fox G (2014) Study on parallel SVM based on MapReduce. http://www.academia.edu/2929779/Study_on_Parallel_SVM_Based_on_MapReduce. Accessed 19 May 2015
• Çatak FO, Balaban ME (2012) CloudSVM: training an SVM classifier in cloud computing systems. In: Proceedings of the pervasive computing and the networked world—joint international conference (ICPCA/SWS), pp 57–68
• Keerthi S, Shevade S, Bhattacharyya C, Murthy K (2001) Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput 13(3):637–649
  Article MATH Google Scholar
• Ghemawat S, Gobioff H, Leung S (2003) The Google file system. In: Proceedings of the 19th ACM symposium on operating systems principles (SOSP), pp 29–43
• Liu Y, Li M, Khan M, Qi M (2014) A MapReduce based distributed LSI for scalable information retrieval. Comput Inf 33(2):259–280
  Google Scholar
• Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston
  MATH Google Scholar
• Weka 3. http://www.cs.waikato.ac.nz/ml/weka. Accessed 19 May 2015
• Sikora T (2001) The MPEG-7 visual standard for content description, an overview. IEEE Trans Circuits Syst Video Technol 11(6):696–702
  Article MathSciNet Google Scholar
• Corel Image Databases. http://www.corel.com. Accessed 19 May 2015
• Lire, An open source Java content based image retrieval library. http://www.semanticmetadata.net/lire/. Accessed 19 May 2015
• Liu Y, Li M, Alham NK, Hammoud S (2013) HSim: a MapReduce simulator in enabling cloud computing. Futur Gener Comput Syst 29(1):300–308
  Article Google Scholar
• Knerr S, Personnaz L, Dreyfus G (1990) Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman Soulié F, Hérault J (eds) Neurocomputing: algorithms, architectures and applications, volume F68 of NATO ASI series. Springer, Berlin, pp 41–50
  Chapter Google Scholar