Least squares twin support vector machines for pattern classification

In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes. Here we attempt to solve two modified primal problems of TSVM, instead of two dual problems usually solved. We show that the solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TSVM. Classification using nonlinear kernel also leads to systems of linear equations. Our experiments on publicly available datasets indicate that the proposed least squares TSVM has comparable classification accuracy to that of TSVM but with considerably lesser computational time. Since linear least squares TSVM can easily handle large datasets, we further went on to investigate its efficiency for text categorization applications. Computational results demonstrate the effectiveness of the proposed method over linear proximal SVM on all the text corpuses considered.     

Planning and execution through variable resolution planning

Generating sequences of actions–plans–for robots using Automated Planning in stochastic and dynamic environments has been shown to be a difficult task with high computational complexity. These plans are composed of actions whose execution might fail due to different reasons. In many cases, if the execution of an action fails, it prevents the execution of some (or all) of the remainder actions in the plan. Therefore, in most real-world scenarios computing a complete and sound (valid) plan at each (re-)planning step is not worth the computational resources and time required to generate the plan. This is specially true given the high probability of plan execution failure. Besides, in many real-world environments, plans must be generated fast, both at the start of the execution and after every execution failure. In this paper, we present Variable Resolution Planning which uses Automated Planning to quickly compute a reasonable (not necessarily sound) plan. Our approach computes an abstract representation–removing some information from the planning task–which is used once a search depth of $k$ steps has been reached. Thus, our approach generates a plan where the first $k$ actions are applicable if the domain is stationary and deterministic, while the rest of the plan might not be necessarily applicable. The advantages of this approach are that it: is faster than regular full-fledged planning (both in the probabilistic or deterministic settings); does not spend much time on the far future actions that probably will not be executed, since in most cases it will need to replan before executing the end of the plan; and takes into account some information of the far future, as an improvement over pure reactive systems. We present experimental results on different robotics domains that simulate tasks on stochastic environments.     

CS-Chord:基于聚类分离的分布式高维向量索引

M-Chord是一种基于P2P网络的高维向量索引,其聚类边缘的向量容易与搜索圆频繁相交,使得查找的区域增多,降低了M-Chord的效率。提出一种基于聚类分离的分布式高维向量索引(CS-Chord),将边缘区域的高频检索向量从Chord环中分离出来,集中存储在服务器上,中心区域的向量仍存储于Chord环中,节省了大量资源的定位时间,从而提高检索效率。实验结果表明:在查询半径为0.2时,CS-Chord距离计算次数约为2000,比M-Chord减少了约2500次;CS-Chord消息转发次数约降低150次,仅为M-Chord的50%。     

Time-local formulation and identification of implicit Volterra models by use of diffusive representation

We present a time-continuous identification method for nonlinear dynamic Volterra models of the form HX=f(u,X)+v with H, a causal convolution operator. It is mainly based on a suitable parameterization of H deduced from the so-called diffusive representation, which is devoted to state representations of integral operators. Following this approach, the complex dynamic nature of H can be summarized by a few numerical parameters on which the identification of the dynamic part of the model will focus. The method is validated on a physical numerical example.     

Conservative and aggressive rough SVR modeling

Support vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use ?-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders.     

Using Semantic Classification Trees for WSD

This paper describes the evaluation of a WSD method withinSENSEVAL. This method is based on Semantic Classification Trees (SCTs)and short context dependencies between nouns and verbs. The trainingprocedure creates a binary tree for each word to be disambiguated. SCTsare easy to implement and yield some promising results. The integrationof linguistic knowledge could lead to substantial improvement.     

蒙古文整词计算机生成理论研究

采用面向对象方法,模拟传统蒙古文整词各种形式构成机理,提出了几种蒙古语整词计算机生成数据模型。文章主要依据整词计算机生成三种模型,探讨了传统蒙古文整词计算机最优化生成理论所涉及的精确度、时间复杂度、空间复杂度三项基本要素以及最优化生成必须考虑的整词复杂特征载荷与一体化合一计算知识表示方法和计算结构,证明了“B - J - T= W”数据模型是传统蒙古文整词计算与生成最优化对象模型。     

一种中文文档的非受限无词典抽词方法

本文提出了一种非受限无词典抽词模型,该模型通过自增长算法获取中文文档中的汉字结合模式,并引入支持度、置信度等概念来筛选词条。实验表明:在无需词典支持和利用语料库学习的前提下,该算法能够快速、准确地抽取中文文档中的中、高频词条。适于对词条频度敏感,而又对计算速度要求很高的中文信息处理应用,例如实时文档自动分类系统。     

Estimation of the Bivariate Stable Spectral Representation by theProjection Method

A method of estimating the spectral representation of a generalized bivariatestable distribution is presented, based on a series of maximum likelihood (ML)estimates of the stable parameters of univariate projections of the data. Thecorresponding stable spectral density is obtained by solving a quadraticprogram. The proposed method avoids the often arduous task of computing themultivariate stable density, relying instead on the standard univariate stabledensity. The paper applies this projection procedure, under the simplifyingassumption of symmetry, to simulated data as well as to foreign exchangereturn data, with favorable results. Kanter projection coefficients governingconditional expectations are computed from the estimated spectral density. For the simulated data these compare well to their known true values.