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This paper presents the novel hierarchical deep neural network (HDNN) for the general multivariate regression problem. The recent insight of deep neural network (DNN) is the deep architecture with large training data can bring the best performance in many research areas. The architecture design of our proposed HDNN focuses on both “depth” and “width” of artificial neural network. Specifically for...
This paper presents a novel approach to writer adaptation based on convolutional neural network (CNN) as a feature extractor and improved discriminative linear regression for online handwritten Chinese character recognition. First, the proposed recognizer consisting of CNN-based feature extractor and prototype-based classifier can achieve comparable performance with the state-of-the-art CNN-based...
This paper presents a discriminative training approach to irrelevant variability normalization (IVN) based joint training of feature transforms and prototype-based classifier for recognition of online handwritten Chinese characters. A sample separation margin based minimum classification error criterion is adopted in IVN-based training, while an Rprop algorithm is used for optimizing the objective...
We present a study of designing compact multiple-prototype based classifiers for rotation-free recognition of online handwritten Chinese characters. Several versions of Rprop algorithms are adopted to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters and a fast-match tree is used...
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