判别信息 meaning in Chinese
discriminant information
Examples
- While flda only can get one optimal discriminating vector , by maximizing the fisher criterion , due to that the rank of the between - class scatter matrix is at most 1 for binary - class problem
但类间离散度矩阵的秩最大只能为1 ，决定了flda只能取一个判别方向(最大特征值对应的特征向量) ，无法利用其它方向的判别信息。 - In early detection of breast cancer , digital ma mmography is considered to be the most reliable method , the presence of microcalcification clusters ( mccs ) is an important sign for the early detection . in this thesis , we first try to extract some useful features of mccs recommended by experts , and then perform classification directly by cs - matmhks , consequently , more information can be saved and the rate between fp and fn can be controlled and traded - off
在乳腺癌早期诊断中,乳腺x片被认为是最有效的技术之一,乳腺癌在x线图像下的主要表现是肿块和微钙化点,本文主要是针对微钙化簇,首先提取一系列医学专家认为对分类有用的特征,然后直接用cs - matmhks分类器进行诊断,从而保留了较多的判别信息,同时当提取的特征很多即输入模式维数很大时,用矩阵化算法又降低了发生过拟合( overfitting )的可能性。 - The inherent relationship between fisher linear discriminant analysis and karhunen - loeve expansion is revealed , i . e . , ulda is essentially equivalent to one classical k - l expansion method . moreover , we enhance ulda using the idea of another k - l expansion method , and finally an optimal k - l expansion method is developed
揭示了具有统计不相关性的线性鉴别分析与经典的k - l展开方法的内在关系，即不相关的线性鉴别分析方法与包含在类均值向量中判别信息的最优压缩方法是等价的，并在此基础上导出了一种最优k - l展开方法。 - To breakthrough this notorious limitation , we propose multi - feature flda ( mflda ) by only replacing the original the between - class scatter with a new scatter measure . mflda still keeps its analytical simplicity . additionally , its recognition performance on unseen samples , i . e . , generalization , surpasses that of the original flda classifier , even outperforms svm in some cases
为打破这种限制，本文采用一种新的度量来衡量异类样本的分离程度，取代fisher判别的类间离散度，改进后的判别称之为多特征线性判别，它不仅可以利用多个方向的判别信息，灵活选择判别方向的个数，而且推广能力优于flda ，在某些情形下甚至超过了svm 。 - When taking part in the bci competition iii , t - weighted approach for feature extraction and reinforcement learning of classifier design are proposed . compared to other methods , t - weight approach has the advantages of requiring less a prior knowledge , exploring more information and computing faster . reinforcement learning is an optimization method both model driven and data driven aiming at mining the discriminative information as more as possible , and improving both the fitting and generalization ability of an existing classifier
相比其它特征提取方法, t加权方法具有对先验知识要求少、信息利用充分、计算快速等优点;而分类器设计的强化学习方法是模型驱动与数据驱动相结合的一种分类器优化方法,其思想在于充分挖掘样本判别信息,在已有分类器基础上进一步提高对数据的拟合能力及泛化能力。