Modeling wine preferences by data mining from physicochemical properties

We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

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.     

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%。     

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.     

基于p范式模型的检索

随着电子文本的大量涌现,人们对信息检索工具提出了更高的要求。本文介绍一种扩展的布尔检索模型及其在中文信息检索系统中的应用,并利用相关反馈技术改善检索系统性能。     

单通道TD-SCDMA射频拉远单元

该文设计了一种单通道TD-SCDMA射频拉远单元。首先根据系统要达到的性能指标要求,设计了系统拓扑结构,接着从该系统拓扑结构出发,分析了整个系统的设计思路及实现方案,选择相应的电路模块。测试结果表明设计的系统达到了预期的指标,临近通道功率比小于-40.64 dBc和误差向量幅度为5.08%rms。     

中文问句分类特征的研究

针对"不同的问句分类特征对问句分类的影响不相同,提取和处理这些特征的时间复杂度也不相同"的问题,提取问题疑问词、核心关键词(疑问词的一二级依存词和问句中心语)的主要义原、核心关键词的首义原、问句主谓宾的主要义原、命名实体、名词单(复)数等六种分类特征,采用支持向量机分类算法,对事实疑问句进行不同特征组合的分类对比实验,发现采用词义消岐技术提取的主要义原不仅对分类的准确率影响明显,而且大幅降低特征向量的维数,减少了处理时间。     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.     

一种优化的AP-CAPSA中文文本结构分析算法*

从段落向量空间模型、特征词权重等多个侧面分析比较了已有的文本结构分析算法,提出了优化的AP-CAPSA算法;给出了算法性能的评价准则,并在不同的语料集合上进行了测试。实验表明该方法能够更有效地反映文本的结构,为中文自动摘要系统奠定了重要的理论基础。