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Among the approaches to solve the sorting problems of complex radar emitter signals, extracting then supplementing the novel signal characteristics is an effective one. The features extracted from the slice of ambiguity function main ridge (AFMR) have turned out to be one of the best practicable parameters because their good signal resolution and good ability to resist noise. In order to search the...
According to the off-line handwritten Chinese characters, a classification and recognition method which is combined by pruning FSVM coarse classification and SVM fine classification is proposed in this text. First cut no value minor to reduce the number of support vector machines, and then determine the coarse classification through fuzzy membership when the coarse classification is done. In fine...
We present a study of discriminative training of classifiers using both maximum mutual information (MMI) and minimum classification error (MCE) criteria for online handwritten Chinese/Japanese character recognition based on continuous-density hidden Markov models. It is observed that MCE-trained classifiers can achieve a much higher recognition accuracy than that of MMI-trained ones. Benchmark results...
Gene selection with interpretation is an important problem in the bioinformatics field. A novel approach called sparse maximal margin features is proposed in this paper for gene subsets selection and visualization. Through transforming an dense eigenvalue decomposition problem into the Elastic-Net regularized sparse regression framework, we introduce sparsity constraint into the coefficients, which...
We present a new feature extraction approach to online Chinese handwriting recognition based on continuous-density hidden Markov models (CDHMM). Given an online handwriting sample, a sequence of time-ordered dominant points are extracted first, which include stroke-endings, points corresponding to local extrema of curvature, and points with a large distance to the chords formed by pairs of previously...
As rapid acquisition of large collections of fluorescence microscopy cell images can be automated, large-scale subcellular localizations of GFP-tagged fusion proteins can be practically accomplished. Semi-supervised learning has the potential of using a large set of unlabeled images for the recognition of subcellular organelle patterns, but the performance still has room for improvement. This paper...
In order to extract compact and effective feature to characterize protein structure, this paper presents a feature extraction of protein fold by mapping into 2-D distance matrix which is regarded as gray level image and further analyzed by image processing techniques. Firstly, gray level co-occurrence matrix (CoM) of distance matrix image (DMI) is calculated and its singular values are taken as the...
One of the most important research aims is to understand the relationship between structure and function of protein. Inspired by this motivation, automatic classification of protein structure becomes one of major research approaches. However, how to extract compact and effective feature to characterize protein structure is still a challenge to it. In this paper, 3-D tertiary structure of protein fold...
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