凉山彝语类别量词的特点

凉山彝语(北部方言什乍话)的类别量词很丰富。量词主要来源于名词、动词和描摹词(有人将其归为形容词);抽象的量词由具体的量词引申而来。凉山彝语量词有自己独特的一套语义分类标准,名词所指事物都有不同的属性,从量词归类时所依据的属性可以看到该语言所侧重的事物属性。除了这些最显著的语义特点外,量词还有一些变调特点和句法特点。拷贝型量词也是凉山彝语类别量词的一个重要类型,正如其他的藏缅语族语言一样,它是量词发展的初级阶段。     

敦煌契约中的量词

敦煌遗书中的世俗文献不仅是研究 9、10世纪敦煌地区社会政治经济状况的宝贵资料 ,也是研究汉语语言文字历史的宝贵资料。从语言上看 ,敦煌世俗文献在一定程度上比较真实地反映了 9、10世纪敦煌地区口语的面貌。比如敦煌世俗文献中的契约文书在量词的使用方面 ,就体现出其不同于唐代诗文小说的特点 ,反映了当时敦煌地区口语语法特点的一个侧面。     

Optimal Designs for Binary Logistic Regression with a Qualitative Classifier with Independent Levels

Dose response studies arise in many medical applications. Often, such studies are considered within the framework of binary-response experiments such as success-failure. In such cases, popular choices for modeling the probability of response are logistic or probit models. Design optimality has been well studied for the logistic model with a continuous covariate. A natural extension of the logistic model is to consider the presence of a qualitative classifier. In this work, we explore D-, A-, and E-optimal designs in a two-parameter, binary logistic regression model after introducing a binary, qualitative classifier with independent levels.     

A permutation test approach to the choice of size k for the nearest neighbors classifier

The k nearest neighbors (k-NN) classifier is one of the most popular methods for statistical pattern recognition and machine learning. In practice, the size k, the number of neighbors used for classification, is usually arbitrarily set to one or some other small numbers, or based on the cross-validation procedure. In this study, we propose a novel alternative approach to decide the size k. Based on a k-NN-based multivariate multi-sample test, we assign each k a permutation test based Z-score. The number of NN is set to the k with the highest Z-score. This approach is computationally efficient since we have derived the formulas for the mean and variance of the test statistic under permutation distribution for multiple sample groups. Several simulation and real-world data sets are analyzed to investigate the performance of our approach. The usefulness of our approach is demonstrated through the evaluation of prediction accuracies using Z-score as a criterion to select the size k. We also compare our approach to the widely used cross-validation approaches. The results show that the size k selected by our approach yields high prediction accuracies when informative features are used for classification, whereas the cross-validation approach may fail in some cases.     

基于D-S理论的多特征融合人体检测算法

提出一种基于D-S证据理论的多特征融合人体检测算法.该方法首先使用harr-like特征方法和HOG特征方法提取人体特征,然后利用后验概率密度函数,估计出两个基本概率分配函数,最后应用D-S证据理论中的合成法则,将两个特征的分类器进行融合决策.实验结果表明,本文提出的算法在Inria数据集上取得了较好的效果,满足了一般工程应用的要求.     

秦简中的量词及其历时演变

六批秦简共有量词47个,物量词系统完备,动量词系统已经萌芽。秦简量词研究改写了我们以前从传世文献量词研究中得出的诸多结论,具有历史词汇学价值。与甲金文相比,秦简量词在数量上有了进一步发展,语法化也更进了一步。但量词与名词的搭配还不确定,其内部发展也不平衡,尤其是"数.量.名"结构仅2见,反映了秦简量词发展的不成熟。     

Scrutinizing an algorithmic technique: the Bayes classifier as interested reading of reality

ABSTRACT

This paper outlines the notion of ‘algorithmic technique’ as a middle ground between concrete, implemented algorithms and the broader study and theorization of software. Algorithmic techniques specify principles and methods for doing things in the medium of software and they thus constitute units of knowledge and expertise in the domain of software making. I suggest that algorithmic techniques are a suitable object of study for the humanities and social science since they capture the central technical principles behind actual software, but can generally be described in accessible language. To make my case, I focus on the field of information ordering and, first, discuss the wider historical trajectory of formal or ‘mechanical’ reasoning applied to matters of commerce and government before, second, moving to the investigation of a particular algorithmic technique, the Bayes classifier. This technique is explicated through a reading of the original work of M. E. Maron in the early 1960 and presented as a means to subject empirical, ‘datafied’ reality to an interested reading that confers meaning to each variable in relation to an operational goal. After a discussion of the Bayes classifier in relation to the question of power, the paper concludes by coming back to its initial motive and argues for increased attention to algorithmic techniques in the study of software.     

A classifier for multi-dimensional datasets based on Bayesian multiple kernel grouping learning

This paper proposes an algorithm for the classification of multi-dimensional datasets based on the conjugate Bayesian Multiple Kernel Grouping Learning (BMKGL). Using conjugate Bayesian framework improves the computation efficiency. Multiple kernels instead of a single kernel avoid the kernel selection problem which is also a computationally expensive work. Through grouping parameter learning, BMKGL can simultaneously integrate information from different dimensions and find the dimensions which contribute more to the variations of the outcome for the purpose of interpretable property. Meanwhile, BMKGL can select the most suitable combination of kernels for different dimensions so as to extract the most appropriate measure for each dimension and improve the accuracy of classification results. The simulation results illustrate that our learning process has better performance in prediction results and stability compared to some popular classifiers, such as k-nearest neighbours algorithm, support vector machine algorithm and naive Bayes classifier. BMKGL also outperforms previous methods in terms of accuracy and interpretation for the heart disease and EEG datasets.     

How to build nomogram for type 2 diabetes using a naïve Bayesian classifier technique

In this study, we introduced a method for building a Bayesian nomogram and proposed an appropriate nomogram for type 2 diabetes (T2D) using data from 13,474 subjects collected from the 2013–2015 Korean National Health and Nutrition Examination Survey (KNHANES) data. We identified risk factors related to T2D, proposed a visual nomogram for T2D from a naïve Bayesian classifier model, and predicted incidence rates. Additionally, we computed confidence intervals for the influence of risk factors (attributes) and verified the proposed Bayesian nomogram using a receiver operating characteristic curve. Finally, we compared logistic regression and the Bayesian nomogram for T2D. The results of the analysis of the T2D data showed that the most influential factor among all attributes in the Bayesian nomogram was age group, and the highest risk factor for T2D incidence was cardiovascular disease. Dyslipidemia and hypertension also had significant impacts on T2D incidence while the effects of sex, smoking status, and employment status were relatively small compared to those of other variables. Using the proposed Bayesian nomogram, we can easily predict the incidence rate of T2D in an individual, and treatment plans can be established based on this information.     

意象理论在汉英量词研究中的认知解读

汉英量词数量众多、用法灵活,体现出量词与名词搭配时所形成的纵聚合关系和横组合关系。文章通过汉英对比的研究方法,应用Langacker意象理论中的视角、认知域和突显等理论对汉英量词的纵聚合关系和横组合关系进行认知解读,发现其共性在于汉英民族使用量词时经常存在对相关事物对象进行突显,选择不同的认知域和进行视角转换所致。