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This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple candidates of character patterns are generated by over-segmentation, and sequences of...
Most AdaBoost algorithms for multi-class problems have to decompose the multi-class classification into multiple binary problems, like the Adaboost.MH and the LogitBoost. This paper proposes a new multi-class AdaBoost algorithm based on hypothesis margin, called AdaBoost.HM, which directly combines multi-class weak classifiers. The hypothesis margin maximizes the output about the positive class meanwhile...
Prototype classifiers trained with multi-class classification objective are inferior in pattern retrieval and outlier rejection. To improve the binary classification (detection, verification, retrieval, outlier rejection) performance of prototype classifiers, we propose a one-vs-all training method, which enriches each prototype as a binary discriminant function with a local threshold, and optimizes...
Different biological labs conduct similar experiments on same diseases. It is highly desirable to have a better model based on more experimental results than that on a single result. In this paper, we propose a method for integrating microarray data from multiple sources for building classification models. To test the method, we use three real world microarray data sets from different labs with different...
The accuracy of handwritten Chinese character recognition can be improved by pair discrimination of similar characters. In this paper, we propose a new method for combining the baseline classifier with incomplete pair discriminators to better exploit their complementariness. The outputs of the baseline classifier and pair discriminators are transformed to two-class probabilities, which are then fused...
The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum classification error (MCE). This paper proposes a new prototype learning algorithm based on the minimization of a conditional log-likelihood loss (CLL), called log-likelihood of margin (LOGM). A regularization term is added...
Density estimation in high-dimensional data spaces is a challenge due to the sparseness of data which is known as ldquothe curse of dimensionalityrdquo. Researchers often resort to low-dimensional subspaces for such tasks, while discard the distribution in the complementary subspace. In this paper, we propose a new mixture density model based on pooled subspace. In our method, the Gaussian components...
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