Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Two computational regimes of a single-compartment neuron separated by a planar boundary in conductance space

Recent in vitro data show that neurons respond to input variance with varying sensitivities. Here we demonstrate that Hodgkin-Huxley (HH) neurons can operate in two computational regimes: one that is more sensitive to input variance (differentiating) and one that is less sensitive (integrating). A boundary plane in the 3D conductance space separates these two regimes. For a reduced HH model, this plane can be derived analytically from the V nullcline, thus suggesting a means of relating biophysical parameters to neural computation by analyzing the neuron's dynamical system.     

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.     

A new graph cut-based multiple active contour algorithm without initial contours and seed points

This paper presents a new graph cut-based multiple active contour algorithm to detect optimal boundaries and regions in images without initial contours and seed points. The task of multiple active contours is framed as a partitioning problem by assuming that image data are generated from a finite mixture model with unknown number of components. Then, the partitioning problem is solved within a divisive graph cut framework where multi-way minimum cuts for multiple contours are efficiently computed in a top-down way through a swap move of binary labels. A split move is integrated into the swap move within that framework to estimate the model parameters associated with regions without the use of initial contours and seed points. The number of regions is also estimated as a part of the algorithm. Experimental results of boundary and region detection of natural images are presented and analyzed with precision and recall measures to demonstrate the effectiveness of the proposed algorithm.     

网络教学心得

该文主要讲了网络教学中必须掌握的知识要点。     

可配置流处理器核心级指令设计及相关编译技术研究

针对目前微处理器面对通用性、高性能、功耗效率的矛盾,我们提出了可配置流处理器的解决方案。本文重点研究了可配置流处理器中核心级指令设计及相关的编译技术,其核心设计思想是根据应用的计算特征设计流处理器中的核心级指令集,从而降低指令集硬件资源的需求。     

源程序的非编译成份的度量

非编译成份包括注释、程序书写风格和命名规则等几个部分,其中程序书写风格可分为空白行、行缩进和换行等。合理地使用非编译成份,可以提高程序的可读性、可理解性、可修改性、可维护性等质量特性。然而,只有高质量的非编译成份才能起到期望的效果。本文旨在提出一种非编译成份的评价方法,促进这些成份质量的提高。在介绍每一种非编译成份后,分别提出了针对这些成份的度量方法。最后,采用加权合成的方式,得到非编译成份的整体度量。     

基于数据挖掘的多策略研究生教育课程成绩分析方法研究

本文对研究生教育中的课程考试成绩数据进行了深入分析,提出了一种成绩综合评定回归模型。该模型用数据挖掘中的关联规则分析方法来分析课程之间的相关性,引入了兴趣度来减少无用的规则,并对兴趣度进行了数学分析,最后对某专业的成绩进行了实证分析。     

一种多分类器集成的手写体汉字识别方法

多分类器集成是手写体汉字识别领域的新方向。本文提出的多分类器集成方法通过改进的欧氏距离分类器将待识别汉字分类到某个粗分结果集中,然后根据粗分结果集选择1-N(one-against-rest)的SVM分类器对待识别汉字进行细分,最后用贝叶斯集成两级分类器。实验对国标一级汉字中的1034个手写汉字进行识别,证明了方案的有效性。     

基于DSP的磁流变阻尼器信号预处理

磁流变阻尼器是一种效率高,响应快,耗能低的智能阻尼器,在众多领域具有广泛应用前景.本文设计并实现了以硬件调理加软件调理相结合的磁流变阻尼器传感器信号预处理方案,此方案中硬件部分采用了集成开关电容滤波器MF10,软件部分采用了去极值递推均值滤波并加入线性误差补偿算法,满足了磁流变阻尼器控制器信号预处理的要求,亦可作为其它信号预处理的参考.