关键词： classification   spam host   Spamdexing   web spam   WEBSPAM-UK2006   WEBSPAM-UK2007  

摘要： In this paper, we present recent contributions for the battle against one of the main problems faced by search engines: the spamdexing or web spamming. They are malicious techniques used in web pages with the purpose of circumvent the search engines in order to achieve good visibility in search results. To better understand the problem and finding the best setup and methods to avoid such virtual plague, in this paper we present a comprehensive performance evaluation of several established machine learning techniques. In our experiments, we employed two real, public and large datasets: the WEBSPAM-UK2006 and the WEBSPAM-UK2007 collections. The samples are represented by content-based, link-based, transformed link-based features and their combinations. The found results indicate that bagging of decision trees, multilayer perceptron neural networks, random forest and adaptive boosting of decision trees are promising in the task of web spam classification. [ABSTRACT FROM AUTHOR]

关键词： 西安电子科技大学   机器学习方法   学术交流   生物系统   Human   实验室工作   学术报告   基因变异  

摘要： 美国北卡罗来纳大学施兴华博士日前来西安电子科技大学访问交流并做了题为“A Machine Learning Approach to Understanding Human Genetic Variation”的学术报告，详细介绍了机器学习方法在人类基因变异中的应用，并与师生们进行了学术交流，就相关问题进行了深入讨论，同时介绍了美国北卡罗来纳大学以及自己实验室工作科研的情况．施兴华博士主要研究在系统层面基因结构对生物过程及复杂表型的影响，研究工作涉及复杂疾病、复杂网络分析和生物系统的数学模型．

关键词： 丙型肝炎病毒NS5B聚合酶   非核苷抑制剂   线性逐步回归分析   偏最小二乘法   遗传算法   支持向量机  

摘要： 对89个苯并异噻唑和苯并噻嗪类丙型肝炎病毒(HCV)NS5B聚合酶非核苷抑制剂进行了定量构效关系(QSAR)研究.采用遗传算法组合偏最小二乘(GA-PLS)和线性逐步回归分析(LSRA)两种特征选择方法选择最优描述符子集,然后建立多元线性回归和偏最小二乘线性回归模型.并首次尝试使用遗传算法耦合支持向量机方法(GA-SVM)对两种特征选择方法所选的描述符子集分别建立非线性支持向量机回归模型.三种机器学习方法所建模型均得到比较满意的预测效果.采用LSRA所选的6个描述符建立的三个QSAR模型对于测试集的相关系数为0.958-0.962,GA-SVM法给出最好的预测精度(0.962).采用GA-PLS所选的7个描述符建立的三个QSAR模型对于测试集的相关系数为0.918-0.960,偏最小二乘回归模型的结果最好(0.960).本工作提供了一种有效的方法来预测丙型肝炎病毒抑制剂的生物活性,该方法也可以扩展到其他类似的定量构效关系研究领域.

关键词： 电磁辐射源   支持向量机   识别方法   辐射源识别   机器学习方法   识别精度   人工神经网络   电磁干扰  

摘要： Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference *** discriminative capability of machine learning methods has recently been used for facilitating *** paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation *** information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the *** model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are *** proposed method has good performance in noisy and reverberant *** has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 *** proposed method has better accuracy in a noisy environment than artificial neural *** that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.

关键词： 数据隐藏   可逆   插值   直方图平移  

摘要： 提出一种基于插值预测和块分类模型的低失真可逆数据隐藏算法。首先,对原始图像进行插值预测,得到预测误差图像;然后根据误差图像建立直方图,使得产生的直方图更陡峭,峰值点出现频率更大;接着对误差图像进行分块,利用块分类模型,在隐藏块内利用直方图平移隐藏信息,尽可能地减少平移点的数量;最后,相应地修改原始像素的灰度值,得到低失真的隐秘图像。实验结果表明,本文算法不仅可以实现可逆数据隐藏,而且隐藏效果较好,有效载荷较高。

关键词： 黑色素瘤   计算机辅助诊断   机器学习  

摘要： 黑色素瘤的计算机辅助诊断是基于激光共聚焦扫描显微镜(CLSM)皮肤图像纹理特征,并引入机器学习的技术,为临床应用研发的一种能够准确、有效地识别在体恶性黑色素瘤新医学诊断方法,将常用的基于机器学习的ID3、分类与回归树(CART)和AdaBoost三种算法应用于良恶性黑色素瘤图像的特征识别,并对各种学习方法的性能进行比较。实验结果表明,AdaBoost算法具有较好的分类识别性能,不但提高了恶性黑色素瘤早期诊断的准确度,降低了良性黑色素瘤的误诊率,而且为临床上早期发现和诊断提供了客观依据。

关键词： Classification   hierarchical forecasting   Machine Learning   Non-normal demand  

摘要： Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative perfonnance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors. [ABSTRACT FROM AUTHOR]

关键词： CLASSIFICATION   DESCRIPTORS  

摘要： Background: A growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors. Results: In this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance. Conclusions: The level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening.

关键词： 机器学习   计算机科学   学习机理   策略研究  

摘要： 近年来机器学习方法在诸多领域得到成功的应用与发展,已成为计算机科学的研究热点之一。本文介绍了机器学习的定义、发展历史与学习策略分类,并对目前存在的问题与挑战进行了分析与展望。

关键词： 机器学习方法   统计分析   网络分析   计算机科学   生物信息学   系统生物学   结构信息   图形结构  

摘要： 图形结构被用于计算机可以识别的结构信息时，对图形信息进行统计分析就成为可能。生物信息学、分子与系统生物学、理论物理、计算机科学、化学、工程等多个领域都在利用这一特点充分发挥计算机在分析和统计方面的优势。本书的一个重要特点就是将诸如图论、机器学习及统计数据分析之类的理论相互结合，