Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Polynomial-Time Decomposition Algorithms for Support Vector Machines
Machine Learning
A parallel mixture of SVMs for very large scale problems
Neural Computation
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Knowledge and Information Systems
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications
Dynamic classifier integration method
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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Classification of different kinds of space objects plays an important role in many astronomy areas Nowadays the classification process can possibly involve a huge amount of data It could take a long time for processing and demand many resources for computation and storage In addition, it may also take much effort to train a qualified expert who needs to have both the astronomy domain knowledge and the capability to manipulate the data This research intends to provide an efficient, scalable classification system for astronomy research We implement a dynamic classification framework and system using support vector machines (SVMs) The proposed system is based on a large-scale, distributed storage environment, on which scientists can design their analysis processes in a more abstract manner, instead of an awkward and time-consuming approach which searches and collects related subset of data from the huge data set The experimental results confirm that our system is scalable and efficient.