An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Leave-One-Out Support Vector Machines
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Clustering of time-course gene expression data using functional data analysis
Computational Biology and Chemistry
Temporal gene expression classification with regularised neural network
International Journal of Bioinformatics Research and Applications
Computational Biology and Chemistry
Temporal gene expression profiles reconstruction by support vector regression and framelet kernel
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Enhanced classification for high-throughput data with an optimal projection and hybrid classifier
International Journal of Data Mining and Bioinformatics
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Classification problems have received considerable attention in biological and medical applications. In particular, classification methods combining to microarray technology play an important role in diagnosing and predicting disease, such as cancer, in medical research. Primary objective in classification is to build an optimal classifier based on the training sample in order to predict unknown class in the test sample. In this paper, we propose a unified approach for optimal gene classification with conjunction with functional principal component analysis (FPCA) in functional data analysis (FNDA) framework to classify time-course gene expression profiles based on information from the patterns. To derive an optimal classifier in FNDA, we also propose to find optimal number of bases in the smoothing step and functional principal components in FPCA using a cross-validation technique, and compare the performance of some popular classification techniques in the proposed setting. We illustrate the propose method with a simulation study and a real world data analysis.