Abstract: Feature extraction and selection in the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS) type ...
Abstract: This paper presents a dual-optimization learning model combining genetic algorithms for global path planning with local obstacle avoidance algorithms for robot navigation in dynamic ...
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