Nonlinear optimization: complexity issues
Nonlinear optimization: complexity issues
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Provably Fast Training Algorithms for Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
K-means Clustering Algorithm for Categorical Attributes
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Shrinkage estimator generalizations of Proximal Support Vector Machines
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An Effective Support Vector Machines (SVMs) Performance Using Hierarchical Clustering
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Mining Protein Sequence Motifs Representing Common 3D Structures
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
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Support Vector Machines (SVMs) are new generation of machine learning techniques and have shown strong generalization capability for many data mining tasks. SVMs can handle nonlinear classification by implicitly mapping input samples from the input feature space into another high dimensional feature space with a nonlinear kernel function. However, SVMs are not favorable for huge datasets with over millions of samples. Granular computing decomposes information in the form of some aggregates and solves the targeted problems in each granule. Therefore, we propose a novel computational model called Clustering Support Vector Machines (CSVMs) to deal with the complex classification problems for huge datasets. Taking advantage of both theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. This feature makes learning tasks for each CSVMs more specific and simpler. Moreover, CSVMs built particularly for each granule can be easily parallelized so that CSVMs can be used to handle huge datasets efficiently. The CSVMs model is used for predicting local protein tertiary structure. Compared with the conventional clustering method, the prediction accuracy for local protein tertiary structure has been improved noticeably when the new CSVM model is used. The encouraging experimental results indicate that our new computational model opens a new way to solve the complex classification for huge datasets.