An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A parallel mixture of SVMs for very large scale problems
Neural Computation
A parallel solver for large quadratic programs in training support vector machines
Parallel Computing - Special issue: Parallel computing in numerical optimization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Image recognition for digital libraries
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Semantic image classification with hierarchical feature subset selection
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Google's MapReduce programming model – Revisited
Science of Computer Programming
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Speed Up SVM Algorithm for Massive Classification Tasks
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Pro Hadoop
A fast parallel optimization for training support vector machine
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Evaluating machine learning techniques for automatic image annotations
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Face detection using spectral histograms and SVMs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
Fast Modular network implementation for support vector machines
IEEE Transactions on Neural Networks
Parallel sequential minimal optimization for the training of support vector machines
IEEE Transactions on Neural Networks
Monocular vision based 6D object localization for service robot's intelligent grasping
Computers & Mathematics with Applications
MRKDSBC: a distributed background modeling algorithm based on mapreduce
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Enhancing genetic algorithms for dependent job scheduling in grid computing environments
The Journal of Supercomputing
DLPR: a distributed locality preserving dimension reduction algorithm
IDCS'12 Proceedings of the 5th international conference on Internet and Distributed Computing Systems
Scalable RDF graph querying using cloud computing
Journal of Web Engineering
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Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) have been used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents MRSMO, a MapReduce based distributed SVM algorithm for automatic image annotation. The performance of the MRSMO algorithm is evaluated in an experimental environment. By partitioning the training dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computers, the MRSMO algorithm reduces the training time significantly while maintaining a high level of accuracy in both binary and multiclass classifications.