关键词： 支持向量机   L2-软间隔   积极集法   加权Parzen窗   分解迭代  

摘要： 以往的工作中,大部分的研究和应用都是基于 L1-软间隔支持向量机方法的.该文首先较为系统地讨论了 L2-软间隔支持向量机方法,探讨了它与 L1-软间隔支持向量机方法相区别的一些性质,如支持向量分布和几何特性等.在实现算法上,该文针对 L2-软间隔支持向量机方法提出了一种基于积极集法的快速支持向量机训练算法,该算法采用了分解迭代的策略,使用积极集方法来求解二次规划子问题,有效地简化了支持向量机的实现步骤.在多个标准数据集上的数值实验表明,该算法对正则参数C<\'\'>的变化不敏感,且在正则参数取值较大C<\'\'>>10时,新算法所需的训练时间比经典的 SVM<\'light>算法要少的多,而得到的预测准确率可以与基于 L1-软间隔支持向量机的算法相比拟.此外,该文从几何直观角度研究了 L2-软间隔支持向量机方法,改进了Keerthi等人提出的最小模算法.使得该算法在所需计算量上可以与近来Keerthi等人提出的最近点对算法(即NPA算法)相比拟,但在编码实现上要简单得多.该文进一步研究了大间隔分类方法.将 L2-软间隔方法与Kernel-Adatron算法相结合,改进了Kernel-Adatron算法,使其不再受样本在特征空间中是线性可分的假设的约束,从而在特征空间中仍然得到统计力学对其收敛速度的理论保证.同时改进了Minover方法,给出了一种加快Kernel-Adatron算法收敛速度的新方法.最后,该文研究了一种基于加权Parzen窗的数据聚类方法.该方法放宽了Parzen窗的权系数限制,采用加权Parzen窗获得对数据分布状态的良好描述,并根据数据的分布状态得到不同模式类的分界面,将聚类过程转变为求解加权Parzen窗权值的线性规划问题.

关键词： MACHINE learning   MACHINE theory  

摘要： Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin ("assignment tests"). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high F-ST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0-2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to "learn" and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks.

关键词： 知识发现   数据挖掘   Web挖掘   XML   数据库   统计分析方法   机器学习方法  

摘要： 在总结KDD方法和DM技术的基础上 ,对web挖掘的现状。

关键词： Markov random fields (MRIF)   spatial autoregression (SAR)   spatial context   spatial data mining  

摘要： Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression (SAR) model, which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas (e.g., solution procedures). We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF. The relationship between SAR and MRF is analogous to the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework.

关键词： 机器学习   软件自动化   算法构架  

摘要： 随着计算机科学的发展各领域之间的相互渗透逐渐形成了许多交叉研究方向 ,基于此本文给出软件自动化系统中的机器学习方法。主要讨论两个方面的问题 :一是软件自动化中的机器学习 。

关键词： 网络攻击   检测系统   机器学习技术   网络信息系统   安全防护  

摘要： 在详细介绍网络攻击检测系统机器学习原理的基础上,对现有的各种方法进行了评述,并结合网络攻击检测系统的应用需求,阐述了网络攻击检测系统机器学习技术的发展方向。

关键词： machine learning   decision support   image classification   neural networks   support vector machines  

摘要： We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs). decision tress. and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi. dysplastic nevi. or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma. the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression. ANNs. and SVMs performed on about the same level. with k-nearest neighbors and decision trees performing worse, (C) 2001 Academic Press.

关键词： brown bear   habitat   machine learning   GIS   Slovenia  

摘要： In this paper we attempt to identify brown bear (Ursus arctos) habitat in south-western part of Slovenia, a country lying on the north-western-most edge of the continuous Dinaric-Eastern Alps brown bear population. The knowledge base (in the form of a decision tree) for the expert system for identifying the suitable habitat, was induced by automated machine learning from recorded bear sightings, and then linked to the GIS thematic layers for subsequent habitat/non-habitat classification of the entire study area. The accuracy of the decision tree classifier was 87% (KHAT 73%). The decision tree mostly agreed with the existing domain knowledge. For the study area the main factors considered by the expert system to be important for brown bear habitat were the percentage of forest (positive), proximity to settlements (negative) and elevation above see (positive), however the decision tree did not account for habitat patch size. After filtering out habitat patches smaller than 5000 ha in GIS, the accuracy increased to 89% (KHAT 77%). Whereas 88% of the habitat was within forests, only 33% of all forests were considered suitable as habitat. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： 支持向量机   正则化网络   非监督学习   油藏描述  

摘要： 基于核函数的机器学习方法是近年来随着支持向量机方法的兴起而引入的一种新的概念.它很好的将输入空间的非线性问题转换成高维特征空间的线性问题加以解决,克服了数据在高维空间中计算内积所带来的困难.该文较为系统地研究了基于核函数的机器学习方法,同时对于它在实际应用中所遇到的诸多问题进行了较为深入的探索.

关键词： 协调机器学习   人工智能   数学模型   多Agent协调  

摘要： 就协调机器学习进行讨论。