On many‐sorted algebraic closure operators

A theorem of Birkhoff‐Frink asserts that every algebraic closure operator on an ordinary set arises, from some algebraic structure on the set, as the corresponding generated subalgebra operator. However, for many‐sorted sets, i.e., indexed families of sets, such a theorem is not longer true without qualification. We characterize the corresponding many‐sorted closure operators as precisely the uniform algebraic operators. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The effect of voice lessons on the clinical and perceptual skills of graduate students in speech-language pathology

Two groups of 10 speech-language pathology graduate students were each given 7 weeks of singing lessons to determine whether voice lessons could have an effect on their clinical and perceptual skills. Pre-, mid-, and posttests to measure various skills were designed and implemented. With use of paired sample statistical testing, statistically significant results were obtained. In addition, the subjective responses of the students show that the lessons were effective in improving pitch perception, breath control, and legato production or easy onset. This study supports efforts to integrate curricula in vocal performance and speech-language pathology.     

Conditional expectation of integrands and random sets

The conditional expectation of integrands and random sets is the main tool of stochastic optimization. This work wishes to make up for the lack of real synthesis about this subject. We improve the existing hypothesis and simplify the corresponding proofs. In the convex case we especially study the problem of the exchange of conditional expectation and subdifferential operators.     

凸域内弦的平均长度

本文研究了凸域内弦的平均长度.通过广义支持函数与凸域的弦幂积分,建立了凸域内弦的平均长度的一般公式,并用此公式得出了圆域和矩形域内弦的平均长度.     

Feature Selection using Fuzzy Support Vector Machines

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.     

REM树脂的制备及亲核加成应用研究

用Mitsunobu反应及丙烯酰氯酰化反应制备了用于固相有机合成的载体REM树脂，将REM树脂与苄胺加成，考查了所合成REM树脂的反应性能，并通过元素分析测定氮含量，推出REM树脂的烯丙基固载量。利用所合成的REM树脂进行了与胺、酚的加成反应动力学研究。结果表明，REM树脂与脂肪族伯胺和促胺的加成反应能够进行，与苯胺不发生反应；REM树脂与酚不能发生亲核加成反应。     

基于SVM局部后验概率融合方法研究

研究了基于支持向量机的后验概率的应用,提出了对样本集进行分解,以产生局部后验概率,根据模式的稳健性对局部后验概率进行凸组合融合的方法.     

F150交换机告警关联规则的研究

为了提高电信网络运营效率，降低维护成本，电信运营企业在网络告警管理系统中需要引入数据挖掘技术。本文主要研究利用数据挖掘技术来进行故障的管理，即对告警进行关联性分析，详细分析了告警序列数据的关联规则挖掘算法，并在南昌本地网F150交换机的告警数据库中予以实现，同时对实际挖掘结果进行分析和整理，推导出一些实用的关联规则。     

完全可分的三元系TS(v,4)的支撑集

§ 1　 IntroductionA triple system of order v and indexλ,denoted by TS(v,λ) ,is a collection of3- ele-mentsubsets Aof a v- set X,so thatevery 2 - subsetof X appears in preciselyλ subsets of A.L etλ≥ 2 and (X,A) be a TS(v,λ) .If Acan be partitioned into t(≥ 2 ) parts A1,A2 ,...,Atsuch that each (X,Ai) is a TS(v,λi) for 1≤ i≤ t,then (X,A) is called de-composable.Otherwise it is indecomposable.If t=λ,λi=1for 1≤ i≤ t,the TS(v,λ) (X,A) is called completely decomposable.It …     

A new type SVM-projected SVM

Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.