The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The Journal of Machine Learning Research
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Spatio-Temporal Patterns in Climate Data Using Clustering
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A survey of kernel and spectral methods for clustering
Pattern Recognition
Kernel-based algorithm for clustering spatial data
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
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This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. For this purpose, a robust weighted kernel k-means algorithm incorporating spatial constraints is presented. The algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.