On the VC Dimension of Bounded Margin Classifiers

In this paper we prove a result that is fundamental to the generalization properties of Vapnik's support vector machines and other large margin classifiers. In particular, we prove that the minimum margin over all dichotomies of k n + 1 points inside a unit ball in R ⁿ is maximized when the points form a regular simplex on the unit sphere. We also provide an alternative proof directly in the framework of level fat shattering.     

复杂系统建模--高维特征空间变量法

本文运用模式识别技术提出了一种分析和选择复杂系统变量的方法.运用该方法使得系统的建模和控制简化.本文以复杂碳化过程作为实例,用模式识别技术对其内在规律进行分析,找出了温度分布与结晶质量的定量关系.在此基础上,建立了碳化过程的动态模型.实践表明,该模型与实际过程基本吻合,并已取得明显经济效益.     

Feature Competitive Algorithm for Dimension Reduction of the Self-Organizing Map Input Space

The self-organizing map (SOM) can classify documents by learning about their interrelationships from its input data. The dimensionality of the SOM input data space based on a document collection is generally high. As the computational complexity of the SOM increases in proportion to the dimension of its input space, high dimensionality not only lowers the efficiency of the initial learning process but also lowers the efficiencies of the subsequent retrieval and the relearning process whenever the input data is updated. A new method called feature competitive algorithm (FCA) is proposed to overcome this problem. The FCA can capture the most significant features that characterize the underlying interrelationships of the entities in the input space to form a dimensionally reduced input space without excessively losing of essential information about the interrelationships. The proposed method was applied to a document collection, consisting of 97 UNIX command manual pages, to test its feasibility and effectiveness. The test results are encouraging. Further discussions on several crucial issues about the FCA are also presented.     

Dimension Reduction and Construction of Feature Space for Image Pattern Recognition

We mathematically and experimentally evaluate the validity of dimension-reduction methods for the computation of similarity in image pattern recognition. Image pattern recognition identifies instances of particular objects and distinguishes differences among images. This recognition uses pattern recognition techniques for the classification and categorisation of images. In numerical image pattern recognition techniques, images are sampled using an array of pixels. This sampling procedure derives vectors in a higher-dimensional metric space from image patterns. To ensure the accuracy of pattern recognition techniques, the dimension reduction of the vectors is an essential methodology since the time and space complexities of processing depend on the dimension of the data. Dimension reduction causes information loss of topological and geometrical features of image patterns. Through both theoretical and experimental comparisons, we clarify that dimension-reduction methodologies that preserve the topology and geometry in the image pattern space are essential for linear pattern recognition. For the practical application of methods of dimension reduction, the random projection works well compared with downsampling, the pyramid transform, the two-dimensional random projection, the two-dimensional discrete cosine transform and nonlinear multidimensional scaling if we have no a priori information on the input data.     

Method for Reducing the Feature Space Dimension in Speech Emotion Recognition Using Convolutional Neural Networks

Automation and Remote Control - We consider the architectures of convolutional neural networks used to assess the emotional state of a person by their speech. The problem of increasing the...     

MPLS中的标签空间及其度量

多协议标记交换技术 (MPLS)是对现有IP组网模式的革新 ,在设计规划网络时它们的主要步骤有许多共同之处 ,但是对标签空间的度量则是MPLS所特有的。对标签空间及其使用进行了分析 ,并给出了标签空间的度量方法。     

基于线性分类器的混合空间查找表颜色分类方法

提出了一种基于线性分类器的混合颜色空间查找表颜色分类方法,该方法主要解决颜色查找表分类方法的区分能力受颜色空间选择、阈值确定等因素影响而难以区分近似颜色的问题。将模式识别中的线性分类器思想应用于颜色查找表映射关系的建立,并通过同时使用HSI空间与YUV空间的方法提高查找表对近似颜色的区分能力。实验结果表明,基于线性分类器的混合空间查找表颜色分类方法具有查找表建立原则简单、效果直观的特点,并且对近似颜色有较强的区分能力,适用于彩色图像的快速颜色分割。     

高维特征空间中文本聚类研究

依据信息论的思想,从文档信息量变化的角度对文本聚类的过程进行了分析,指出了信息量在聚类过程中呈现的规律性,进而提出一种基于信息量模型的聚类分析算法。通过对高维特征空间中影响聚类准确率因素的分析,发现特征之间复杂的语义联系和过高的维度是影响文本聚类准确率的重要因素。从削弱特征之间的语义联系入手,提出了一种特征聚类算法,其算法复杂度与处理的文档数量无关,提高了高维空间下聚类的速度和效果。两种算法的结合使得对大量高维文本数据直接聚类变得可行,实际的测试中获得了满意的效果。     

Effective Feature Extraction in High-Dimensional Space

This correspondence first kernalizes the region covariance matrix and formalizes the similarity metric using four block matrices. The effectiveness of the proposed methods is proven with experiments on face recognition.      

虚拟战场环境中地物的生成

在分布式交互仿真中,虚拟战场环境的质量极大地影响了训练的逼真度,而地物是影响虚拟战场环境质量的重要因素.但是在实际应用中地物的生成方式不够灵活、高效,操作上也不够方便.为了达到快速生成虚拟战场环境的目的,设计了地物生成模块,该文给出了虚拟战场环境中地物的详细分类,并介绍了地物生成的技术,给出了地物生成模块中参数的具体定义和地物生成的实现方式.该模块已经应用到装甲兵装备作战仿真实验室的建设中,收到良好的效果.     

基于线性投影的代数空间降维分析

主成分分析和奇异值分解都可以用于代数空间降维的线性投影分析,该文详细分析了这两种代数方法并给出了用于代数空间降维分析时二者之间的联系,并得到了在正定的实对称矩阵条件下主成分分析和奇异值分解是等价的这一结论。     

Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension

In this paper we study a Bayesian or average-case model of concept learning with a twofold goal: to provide more precise characterizations of learning curve (sample complexity) behavior that depend on properties of both the prior distribution over concepts and the sequence of instances seen by the learner, and to smoothly unite in a common framework the popular statistical physics and VC dimension theories of learning curves. To achieve this, we undertake a systematic investigation and comparison of two fundamental quantities in learning and information theory: the probability of an incorrect prediction for an optimal learning algorithm, and the Shannon information gain. This study leads to a new understanding of the sample complexity of learning in several existing models.     

Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs

There is a commonly held opinion that the algorithms for learning unrestricted types of Bayesian networks, especially those based on the score+search paradigm, are not suitable for building competitive Bayesian network-based classifiers. Several specialized algorithms that carry out the search into different types of directed acyclic graph (DAG) topologies have since been developed, most of these being extensions (using augmenting arcs) or modifications of the Naive Bayes basic topology. In this paper, we present a new algorithm to induce classifiers based on Bayesian networks which obtains excellent results even when standard scoring functions are used. The method performs a simple local search in a space unlike unrestricted or augmented DAGs. Our search space consists of a type of partially directed acyclic graph (PDAG) which combines two concepts of DAG equivalence: classification equivalence and independence equivalence. The results of exhaustive experimentation indicate that the proposed method can compete with state-of-the-art algorithms for classification.Editors: Pedro Larrañaga, Jose A. Lozano, Jose M. Peña and Iñaki Inza     

Special Feature: Studying the Oceans from Space

Three high-speed data streams from a new ocean-monitoring satellite are computer-processed on the ground into meaningful geophysical and meteorological information.     

Achieving Robustness and Minimizing Overhead in Parallel Algorithms Through Overlapped Communication/Computation

One of the major goals in the design of parallel processing machines and algorithms is to achieve robustness and reduce the effects of the overhead introduced when a given problem is parallelized or a fault occurs. A key contributor to overhead is communication time, in particular when a node is faulty and another node is substuiting for its operation. Many architectures try to reduce this overhead by minimizing the actual time for a communication to occur, including latency and bandwidth figures. Another approach is to hide communication by overlapping it with computation assuming that the computation is the most prominent factor. This paper presents the mechanisms provided in the Proteus parallel computer and its effective use of communication hiding through overlapping communication/computation techniques with and without the presence of a fault. These techniques are easily extended for use in compiler support of parallel programming. We also address the complexity (or rather simplicity) in achieving complete exchange on the Proteus Machine.     

分型维数特征量对病变组织的超声定征

按照Ｍａｎｄｅｌｂｒｏｔ的分形理论,医学图象及许多自然图象的灰度表面的形成均符合分形布朗运动规律。而且可以用分形的维数来表征图象灰度表面的精细与粗糙程度。文中正是基于这种思想,采用图象的分形维数作为一个特征参量,对人体的肌肉组织进行超声定征。     

基于特征分类的MRI医学图像弹性配准

基于互信息的弹性图像配准是医学图像配准的重要方法之一。然而由于互信息在小样本图像配准中,会出现多局部极值和极值偏离问题,从而容易出现配准误差,进而造成整图的弹性配准误差。为减少这种配准误差,提出了一种基于特征分类的互信息医学图像弹性配准方法。该方法先采用图像的灰度和梯度特征训练自组织映射(self-organized mapping,SOM)神经网络特征分类器,将图像由高维灰度空间映射到低维特征类别空间;然后,在特征类别空间进行互信息图像弹性配准。实验结果表明,该方法大大提高了小样本图像配准的成功率,并可通用于有噪和无噪的医学图像弹性配准中。