查询词典 coarse classifier

This article mainly introduced Ningyuan's geographical position, in the county boundary the dialect atlas situation, separately from aspects and so on noun, verb, adjective, adverb, preposition and classifier showed its characteristic and pointed out the example to come to compare, has utilized the comparison test, through the Ningyuan dialect and the standard spoken Chinese comparison, the investigation and study, discovered that the Ningyuan dialect and the standard spoken Chinese in the glossary difference, introduced the Ningyuan dialect glossary the general picture, showed in the Ningyuan dialect glossary some characteristics, the discussion dialect glossary and the dialect glossary research work important meaning

本文主要介绍了宁远的地理位置、县境内方言分布情况，分别从名词、动词、形容词、副词、介词和量词等方面说明其特点并举出实例来加以比较，运用了比较法，通过宁远方言与普通话比较、调查研究，发现宁远方言与普通话在词汇上的差别，来介绍宁远方言词汇的概貌，说明宁远方言词汇上的一些特点，讨论方言词汇及方言词汇调查研究工作的重要意义。

Among used machine learning methods, the gradient descent method is widely used to train various classifiers, such as Back-propagation neural network and linear text classifier. However, the gradient descent method is easily trapped into a local minimum and slowly converges. Thus, this study presents a gradient forecasting search method based on prediction methods to enhance the performance of the gradient descent method in order to develop a more efficient and precise machine learning method for Web mining.However, a prediction method with few sample data items and precise forecasting ability is a key issue to the gradient forecasting search method. Applying statistic-based prediction methods to implement GFSM is unsuitable because they require a large number of data items to model a prediction model. In the contrast with statistic-based prediction methods, GM(1,1) grey prediction model does not need a large number of data items to build a prediction model, and it has low computational load. However, the original GM(1,1) grey prediction model uses a mathematical hypothesis and approximation to transform a continuous differential equation into a discrete difference equation in order to model a forecasting model.

其中梯度法是一个最常被使用来实现机器学习的方法之一，然而梯度法具有学习速度慢以及容易陷入局部最佳解的缺点，因此，本研究提出一个梯度预测搜寻法则(gradient forecasting search method， GFSM)来改善传统梯度法的缺点，用来提升一些以梯度学习法则为基础的分类器在资讯探勘上的效率与正确性；而一个所需资料量少、计算复杂度低且精确的预测模型是梯度预测搜寻法能否有效进行最佳解搜寻之关键因素，传统统计为基础之预测方法的缺点是需要较大量的数据进行预测，因此计算复杂度高，灰色预测模型具有建模资料少且计算复杂度低等优点，然而灰色预测理论以连续之微分方程式为基础，并且透过一些数学上的假设与近似，将连续之微分方程式转换成离散之差分方程式来对离散型资料进行建模及预测，这样的作法不尽合理，且缺乏数学理论上的完备性，因为在转换过程中已经造成建模上的误差，且建模过程仅考虑相邻的两个资料点关系，无法正确反应数列未来的变化趋势。

The Directory Classifier listing can be saved as a file that can be imported into most popular spreadsheets and database applications.

目录分类上市，可以节省作为一个文件可以导入到最流行的电子表格和数据库应用程序。

Discriminant classifier is a type of supervised machine learning technique. There are two approaches to it.

区别分类器是一种已知既有类别的机器学习技术。

We give a classifier design method based on sun-class division, bring forward a new divisibility rule, and can ascertain the number of sub-class by training.

给出了一种基于子类划分的分类器设计方法，提出了一个基于类内散布矩阵和类间散布矩阵的新的可分性准则，可以通过训练自动确定子类个数。

Compared to the study on certain books and dynastic history, Chinese classifier history is still not studied fully.

相比于专书研究和断代研究，目前关于汉语量词史的研究尚不充分。

Because of lesser error probability of Bayesian Classifier , it has extensive application foreground.

贝叶斯分类具有较小的出错率，因而，有着广泛的应用前景。

The fundamental of non-negative matrix factorization algorithm. It is used to extract EEG power spectrum feature. Artificial neural network is employed as classifier.

介绍了非负矩阵分解算法的基本原理，给出一种利用NMF 进行脑电能量谱特征提取的方法。

A classifier ensemble method Gagging based on class information was proposed.

提出一种基于类别信息的分类器集成方法cagging。

Furthermore, we also developed a Bayesian based classifier, GALOP, to predict a protein's subcellular location based on the probabilities of the detected signatures on distinct subcellular locations.

并且，利用已知细胞位置的蛋白质当作机器学习的训练资料，来找寻具有鉴别率的特徵集合。

There are different types of personal loans available.

有许多不同类型的个人贷款优惠。

The above mentioned teaching model of spiral rising can be used to avoid rigidness in teaching.

上述螺旋式上升的教学模式，可以避免教学形成模式後的僵化现象。