AN ADAPTIVELY TRAINED KERNEL-BASED NONLINEAR REPRESENTOR FOR HANDWRITTEN DIGIT CLASSIFICATION

In practice, retraining a trained classifier is necessary when novel data become available. This paper adopts an incremental learning procedure to adaptively train a Kernel-based Nonlinear Representor (KNR), a recently presented nonlinear classifier for optimal pattern representation, so that its generalization ability may be evaluated in time-variant situation and a sparser representation is obtained for computationally intensive tasks. The addressed techniques are applied to handwritten digit classification to illustrate the feasibility for pattern recognition.

A Kernel-Based Nonlinear Representor with Application to Eigenface Classification

This paper presents a classifier named kernel-based nonlinear representor (KNR) for optimal representation of pattern features. Adopting the Gaussian kernel, with the kernel width adaptively estimated by a simple technique, it is applied to eigenface classification. Experimental results on the ORL face database show that it improves performance by around 6 points, in classification rate, over the Euclidean distance classifier.

论意思表示错误的法律行为之效力

意思表示作为法律行为之本质属性,是决定法律行为效力的根本因素。当意思表示主观层面的效果意思或客观层面的外部表示出现错误时,其对相应法律行为之效力会产生一定的影响。传统民法中有规定意思表示错误的法律行为之效力为可撤销,有规定其为无效,实务中还有通过意思表示解释得出其依然有效的结论。为了平衡法律行为双方当事人的利益,保护交易安全,我国未来民法典宜原则上规定意思表示错误的法律行为之效力为可撤销,同时明确意思表示解释在判断其效力上的优先性。