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Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification...
In geographical information engineering, height anomalies must be known in order to convert GPS ellipsoid heights into geodetic heights. There are many conversion models, such as polynomial, BP neural network and multi-quadrics fitting. Because the quasi-geoid is an irregular geometric object, every method has both advantages and disadvantages, and is appropriate to different conversion patterns....
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification problems in the visual domain (such as recognizing handwriting, faces and human-body images) often require a large number of training examples, which may become available over a long training period. This motivates the development...
Thalassemia is a genetic defect that is commonly found in many parts of the world. Number of humans that are suffering from this disease is determined by screening the heterozygous population. This article investigates the thalassemia screening problem using the unconstrained functional networks classifier. The learning algorithm for this new scheme is briefly illustrated. The new intelligent system...
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