Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13368
Title: Recursive pattern based hybrid training
Authors: KIRUTHIKA RAMANATHAN
Keywords: Machine Learning, Genetic Algorithms, Neural Networks, Ensemble Architectures, Clustering, Higher Order Neurons
Issue Date: 27-Sep-2007
Source: KIRUTHIKA RAMANATHAN (2007-09-27). Recursive pattern based hybrid training. ScholarBank@NUS Repository.
Abstract: Data decomposition and ensemble learning have been used in several applications to improve the training time and generalization accuracy of machine learning methods. In these approaches, the number and type of members in the ensemble is known to be an important factor in determining its generalization error. We present in this thesis, a recursive combination of global training and local training for supervised and unsupervised machine learning tasks. The resulting ensemble (also called pseudo global optima) consists of a deterministic number of sub- solutions that, when integrated, are capable of improved generalization with a shorter training time. We demonstrate the algorithm in the domains of curve fitting, classification and clustering. The Recursive Pattern Based Hybrid Training algorithm, when applied to benchmark datasets, resulted in 40% improvement in generalization accuracy for the classification problems tested and 50% improvement in the clustering accuracy for unsupervised learning.
URI: http://scholarbank.nus.edu.sg/handle/10635/13368
Appears in Collections:Ph.D Theses (Open)

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