高考人数见拐点后的高职招生环境考察与预测

通过对我国适龄人口变化趋势的系统考察和2013～2019年全国高考人数的预测,以及目前我国高职院校招生情况的具体分析,揭示了高职院校招生的隐含问题;并针对问题和问题产生的原因,提出了高考人数见拐点后的高职招生应对策略:非学历教育是缓解高考生源危机最主要出路。     

基于主动式边界基元模型的多类目标识别方法

针对形状特征,提出了一种基于主动式边界基元模型的多类目标自动识别方法. 该方法以主动式边界基元为基础构建字典,可准确描述各类目标的形状结构, 不受尺度、旋转等变化的影响;然后,综合分析上下文信息进行概率学习,采用级联框架和Bootstrap动态采样训练最优边界分类器,实现目标的类别识别和位置定位,并可获取精确形状. 实验结果表明,该方法能有效提取多种类型和复杂结构的目标,具有较强的实用价值.     

从物理学方法论看物理学的发展

从物理学方法论的角度对物理学的发展作了回顾和考察,由此得出二点有益的启示:无论在牛顿力学、统计物理还是在量子力学中,决定性描述和概率性描述二者并存;物理学可能面临着第三次大综合.     

走进生活数学 感受概率魅力

随着科学的发展,数学在生活中的应用越来越广,而概率作为数学的一个分支,同样也广泛应用于生活的各个方面。     

Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications

Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define multilingual probabilistic topic modeling (MuPTM) and present the first full overview of the current research, methodology, advantages and limitations in MuPTM. As a representative example, we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA (BiLDA). We provide a thorough overview of this representative multilingual model from its high-level modeling assumptions down to its mathematical foundations. We demonstrate how to use the data representation by means of output sets of (i) per-topic word distributions and (ii) per-document topic distributions coming from a multilingual probabilistic topic model in various real-life cross-lingual tasks involving different languages, without any external language pair dependent translation resource: (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review several other applications present in the relevant literature, and introduce and illustrate two related modeling concepts: topic smoothing and topic pruning. In summary, this article encompasses the current research in multilingual probabilistic topic modeling. By presenting a series of potential applications, we reveal the importance of the language-independent and language pair independent data representations by means of MuPTM. We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross-lingual topics, that is, how to effectively employ learned per-topic word distributions and per-document topic distributions of any multilingual probabilistic topic model in various cross-lingual applications.     

基于依存树库的现代汉语名词语法功能的计量研究

现代汉语自动句法分析需要词类句法功能的量化信息。本文基于概率配价模式理论,利用汉语依存树库,对现代汉语名词的句法功能进行了计量研究。文章把名词各句法功能,按其出现频率的高低,区分出典型功能和非典型功能,给出了名词句法功能的关联标记模式和概率配价模式,从而从定量分析的角度,对前人的研究结论进行了验证和补充,有助于更清晰地认识汉语名词的句法功能,并对对外汉语语法教学提供参考。     

Exploiting Hierarchy in Text Categorization

With the recent dramatic increase in electronic access to documents, text categorization—the task of assigning topics to a given document—has moved to the center of the information sciences and knowledge management. This article uses the structure that is present in the semantic space of topics in order to improve performance in text categorization: according to their meaning, topics can be grouped together into meta-topics, e.g., gold, silver, and copper are all metals. The proposed architecture matches the hierarchical structure of the topic space, as opposed to a flat model that ignores the structure. It accommodates both single and multiple topic assignments for each document. Its probabilistic interpretation allows its predictions to be combined in a principled way with information from other sources. The first level of the architecture predicts the probabilities of the meta-topic groups. This allows the individual models for each topic on the second level to focus on finer discriminations within the group. Evaluating the performance of a two-level implementation on the Reuters-22173 testbed of newswire articles shows the most significant improvement for rare classes.     

概率赋范空间的局部有界与可赋范化

讨论了概率赋范空间的局部有界性,阐明了局部有界的概率赋范空间与赋范空间的关系,给出了概率赋范空间可赋范化的充要条件,提出了广义概率赋范空间和最佳广义弱ｔ－模的概念,在这些新概率之下讨论了一些相应的问题。     

基于Logistic回归模型的博士研究生超期毕业影响因素分析——以哈尔滨工业大学为例

为充分了解和掌握影响博士研究生超期毕业的主要因素,本研究基于Logistic回归模型相关理论构建博士生超期毕业概率模型。以哈尔滨工业大学为研究载体,对构建的博士生超期毕业模型进行实证分析,并得到了影响博士生超期毕业的关键因素。该研究为博士生超期预警机制的建立、博士生的教育管理以及相关政采的制定提供了一定的参考与借鉴。     

亚像素级激光光条提取及其激光平面标定应用(英文)

为了标定激光平面以实现三维形貌测量,提出一种亚像素级精度激光光条提取算法.该算法包括两侧边检测和中心线提取2个部分.首先,利用基于主成分角的渐进概率霍夫变换检测2条侧边并依赖该两侧边的距离获取光条宽度;然后,应用二维泰勒展式提取具有亚像素级精度的光条中心线并依据重建的三维坐标标定激光平面.实验结果表明,所提算法光条中心提取速度较快,平均约为78 ms/帧,光条平面共面误差较低,限制在0.3 mm以内.因而所提算法能够满足提取光条的两侧边和中心线的需要,且快速可靠、精度高和抗干扰能力强.