预分类 meaning in Chinese
presort
Examples
- The approach to classification based on the equivalence classes of the main attribute is as follows : ( 1 ) discretize the continuous training data
以某属性的等价类(相近类)组成的子集作为svm的训练集预分类的方法如下。 ( 1 )将连续性训练数据离散化。 - Firstly , we introduce the basic theory and methods for realization of sofm . subsequently we improve the arithmetic and implement the feature extraction of raw data using k - l translation , select the eigenvector . combining c - avarage and isodata arithmetic , classify the eigenvector , according to the methods of this dissertation , in lower layer , set nerve cell and unite or delete nerve cells in middle layer , to improve the anti - huise - and robust
论文首先介绍了sofm的基本原理和实现方法，接着在其基础上对具体算法进行改进，用k - l变换对原始数据进行特征提取，选取出特征变量，结合c -均值和isodata算法对特征变量进行预分类，按照本文中介绍的方法在底层预置神经元，在中间层对神经原进行合并和删除，加强了该网络的抗干扰性和稳定性。 - Taking the real - time recognition request into consideration , a bi - layer ahmm model ( bl - ahmm ) is introduced . bl - ahmm can reduce the recognition space by pre - categorization , which improves the speed and accuracy of recognition . like the other grads descend algorithms , the problem of local optima is also existed in the training of hmm
为了进一步提高识别速度，满足联机识别中实时性的要求，本文在ahmm的基础上同时提出了一种双层自适应识别模型（ bl - ahmm ） ，通过预分类来减小识别空间，提高了系统识别速度并同时提高了识别率。 - As a currency learning techniques introduced recent years , svms which handle small sample size problem have the features of good generalization ability , solid theoretical background , high accuracy , and getting global optimization . ho vever , it is a classifier for two - class originally and not suite for the multi - class problems and dealing with large data sets . on the other hand , , rst has the features of handling and reducing large data sets while has lower classification accuracy than svms . in this paper , the data are classified in advance with the rst , and two methods of combination of the data to classify two - class problems are proposed
但它是二值分类器，不适用于多值分类场合及处理海量数据。粗集理论则具有处理和约简大数据量的优势，但分类精度不如svm方法。本文利用粗集理论对数据进行预分类，在此基础上提出两种二值分类数据组合方法，然后，再利用svm两两分类。 - Then one against one classification is performed with svms . so the muti - class problem can be solved , the accuracy of classification guaranteed , and the reduction of the data carried out . in particular , the approach to classification based on the equivalence classes of the main attribute is explicit conceptually , easy to understand and implement . furthermore , the reduction of the sample size is distinct
这样，既解决了多值分类问题，提高了分类精度，又实现了数据压缩。其中利用主属性中不可分辨关系(或相近关系)预分类的方法，概念清晰，易于理解、操作，数据压缩量大。