Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss the use of divergences in dissimilarity-based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular,  introduce and study divergence based learning vector quantization (DLVQ).  derive cost function based DLVQ schemes for the family of @c@?divergences which includes the ll-known Kullback-Leibler divergence and the so-called Cauchy-Schwarz divergence as special cases. The corresponding training schemes are applied to two different real world data sets. The first one, a benchmark data set (Wisconsin Breast Cancer) is available in the public domain. In the second problem, color histograms of leaf images are used to detect the presence of cassava mosaic disease in cassava plants.  compare the use of standard Euclidean distances with DLVQ for different parameter settings.  show that DLVQ can yield superior classification accuracies and Receiver Operating Characteristics.
['Ernest Mwebaze', 'Petra Schneider', 'Frank-Michael Schleif', 'Jennifer R. Aduwo', 'John A. Quinn', 'Sven Haase', 'Thomas Villmann', 'Michael Biehl']
I am writing a research paper on a idea Divergence-based classification in learning vector quantization
[{"from": "human", "value": "I am writing a research paper on a idea Divergence-based classification in learning vector quantization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss the use of divergences in dissimilarity-based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular,  introduce and study divergence based learning vector quantization (DLVQ).  derive cost function based DLVQ schemes for the family of @c@?divergences which includes the ll-known Kullback-Leibler divergence and the so-called Cauchy-Schwarz divergence as special cases. The corresponding training schemes are applied to two different real world data sets. The first one, a benchmark data set (Wisconsin Breast Cancer) is available in the public domain. In the second problem, color histograms of leaf images are used to detect the presence of cassava mosaic disease in cassava plants.  compare the use of standard Euclidean distances with DLVQ for different parameter settings.  show that DLVQ can yield superior classification accuracies and Receiver Operating Characteristics."}]