针对支持向量机（Suppon Vector Machine，sVM）对大规模样本分类效率低下的问题，提出了基于自适应共振理论（Adaptive Resonance Theory，ART）神经网络与自组织特征映射（self-Organizingfeature Map，SOM）神经网络的SVM训练算法，分别称为ART-SVM算法与SOM-SVM算法。这两种算法通过聚类压缩数据集，使sVM训练的速度大大提高，同时可获得令人满意的泛化能力。

传统的支撑向量机是一种有监督学习方法，为了提高其泛化能力，提出了一种新的基于多学习器标记的半监督SVM学习方法。该方法将训练集分为有标记样本和无标记样本两个集合，通过对无标记样本进行分别标记，不断修正有标记样本集规模，从而提高SVM的泛化性能。在中文电子邮件过滤数据集上的实验结果证明了该方法的可行性和有效性。

泛化能力是机器学习关心的一个根本问题，采用集成学习技术可以有效地提高泛化能力。本文提出了一种将支持向量机（Support Vector Machine，SVM）进行选择性集成回归的方法。通过引入三个阈值，可以选择合适的子SVM，从而进一步提高了整个集成学习的效率。实验结果表明，本文提出的选择性集成方法可以在一定程度上解决SVM的模型选择问题和大规模数据集的学习问题，与传统的集成方法Bagging相比具有更高的泛化能力。

Basedon statistical learning theory, Support Vector Machine (SVM) is a novel type of learning machine, andit contains polynomial, neural network andrad ial basis function (RBF) as special cases. In the RBF case, the Gaussian kernel is commonly used, while the spread parameter, in the Gaussian kernel is essential to generalization performance of SVMs. In this aper, determination of σ is studiedbasedon discussions of the in8uence ofσon generalization performance. For classification problems, the optimalσcan be computedon the basis of Fisher discrimination. Andfor regression problems, basedon scale space theory, we demonstrate the existence of a certain range ofσ, within which the generalization performance is stable. An appropriateσwithin the range can be achieved via dynamic evaluation. In addition, the lower boundof iterating step size ofσis given. Simulation results show the e:ectiveness of the presentedmethod.

This paper presents an improved incremental learning technique for SVM, namely redundant incremental SVM (RISVM), for pattern classification problems. Through adding some non-support vectors (say, redundant vectors in the sense of contribution to the final solution) at each incremental step, the RISVM algorithm can achieve similar performance to the SVM in batch (or non-incremental SVM) but result in less support vectors for the same quality of pattern classification, and also it can provide better generalization performance in comparison with other incremental techniques for SVM. The bispiral problem and five widely used benchmark data sets are employed to verify the method, and the simulations support the feasibility and effectiveness of the proposed approach.

随着电子邮件的广泛应用，泛滥成灾的垃圾邮件对人们的生活和网络安全带来了严重的威胁，反垃圾邮件问题已成为全球性的具有现实意义的问题。本文提出了一种基于动态特征词典的SVM中文邮件过滤方法，通过动态构造特征词典以及选择合适的支持向量机（Support Vector Machine，SVM）核参数，有效地提高了垃圾邮件的过滤精度，实验结果超过了网易免费邮所公布的过滤指标。

选择是支撑向量机（Support Vector Machine，SVM）研究中的核心问题之一。提出了一种基于数据分布特征的SVM核函数及其参数选择的方法。首先分析了确定数据分布特征的重要性，然后给出了判断数据呈高斯分布的方法，并探讨了SVM核函数及其参数选择与数据分布的相关性。数值实验说明了本文提出的方法的可行性与有效性。

For time-series forecasting problems, there have been several prediction models to data, but the development of a more accurate model is very difficult because of high non-linear and non-stable relations between input and output data. Almost all the models at hand are not applicable online, although online prediction, especially for air quality parameters forecasting, has very important significance for realworld applications. A support vector machine (SVM), as a novel and powerful machine learning tool, can be used for time-series prediction and has been reported to perform well by some promising results. This paper develops an online SVM model to predict air pollutant levels in an advancing time-series based on the monitored air pollutant database in Hong Kong downtown area. The experimental comparison between the online SVM model and the conventional SVM model (non-online SVM model) demonstrates the effectiveness and efficiency in predicting air quality parameters with different time series.

A heuristic method for accelerating support vector machine (SVM) training based on a measurement of similarity among samples is presented in this paper. To train SVM, a quadratic function with linear constraints is optimized. The original formulation of the objective function of an SVM is e6cient during optimization phase, but the yielded discriminant function often contains redundant terms. The economy of the discriminant function of an SVM is dependent on a sparse subset of the training data, say, selected support vectors by optimization techniques. The motivation for using a sparse controlled version of an SVM is therefore a practical issue since it is the requirement of decreasing computation expense during the SVM testing and enhancing the ability to interpretation of the model. Besides the existing approaches, an intuitive way to achieve this task is to control support vectors sparsely by reducing training data without discounting generalization performance. The most attractive feature of the idea is to make SVM training fast especially for training data of large size because the size of optimization problem can be decreased greatly. In this paper, a heuristic rule is utilized to reduce training data for support vector regression (SVR). At ;rst, all the training data are divided into several groups, and then for each group, some training vectors will be discarded based on the measurement of similarity among samples. The prior reduction process is carried out in the original data space before SVM training, so the extra computation expense may be rarely taken into account. Even considering the preprocessing cost, the total spending time is still less than that for training SVM with the complete training set. As a result, the number of vectors for SVR training becomes small and the training time can be decreased greatly without compromising the generalization capability of SVMs. Simulating results show the e=ectiveness of the presented method.

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.