Encyclopedia of Artificial Intelligence最新文献_第6页

Automated Cryptanalysis of Classical Ciphers 经典密码的自动密码分析

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH029

O. Grošek, Pavol Zajac

引用次数: 4

RBF Networks for Power System Topology Verification 用于电力系统拓扑验证的RBF网络

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH199

R. Lukomski, K. Wilkosz

引用次数: 2

Learning in Feed-Forward Artificial Neural Networks I 前馈人工神经网络的学习[j]

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH149

L. B. Muñoz

引用次数: 2

A Study of the Performance Effect of Genetic Operators 遗传算子的性能效应研究

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH220

Pi-Sheng Deng

{"title":"A Study of the Performance Effect of Genetic Operators","authors":"Pi-Sheng Deng","doi":"10.4018/978-1-59904-849-9.CH220","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH220","url":null,"abstract":"Performance of genetic algorithms (GAs) is mainly determined by several factors. Not only the genetic operators affect the performance of a GA with varying degrees, but also the parameter settings for genetic operators interact in a complicated manner with each other in influencing a GA’s performance. Though many studies have been conducted for this cause, they failed to converge to consistent conclusions regarding the importance of different genetic operators and their parameter settings on the performance of GAs. Actually, optimizing the combinations of different strategies and parameters for different problem types is an NPcomplete problem in itself, and is still an open research problem for GAs (Mitchell, 1996). Recognizing the intrinsic difficulties in finding universally optimal parameter configurations for different classes of problems, we advocate the experience-based approach to discovering generalized guiding rules for different problem domains. To this end, it is necessary for us to gain a better understanding about how different genetic operators and their parameter combinations affect a GA’s behavior. In this research, we systematically investigate, through a series of experiments, the effect of GA operators and the interaction among GA operators on the performance of the GA-based batch selection system as proposed in Deng (2007). This paper intends to serve as an initial inquiry into the research of useful design guidelines for configuring GA-based systems.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129484501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble of SVM Classifiers for Spam Filtering 用于垃圾邮件过滤的SVM分类器集成

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH086

Ángela Blanco, M. Martín-Merino

{"title":"Ensemble of SVM Classifiers for Spam Filtering","authors":"Ángela Blanco, M. Martín-Merino","doi":"10.4018/978-1-59904-849-9.CH086","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH086","url":null,"abstract":"Unsolicited commercial email also known as Spam is becoming a serious problem for Internet users and providers (Fawcett, 2003). Several researchers have applied machine learning techniques in order to improve the detection of spam messages. Naive Bayes models are the most popular (Androutsopoulos, 2000) but other authors have applied Support Vector Machines (SVM) (Drucker, 1999), boosting and decision trees (Carreras, 2001) with remarkable results. SVM has revealed particularly attractive in this application because it is robust against noise and is able to handle a large number of features (Vapnik, 1998). Errors in anti-spam email filtering are strongly asymmetric. Thus, false positive errors or valid messages that are blocked, are prohibitively expensive. Several authors have proposed new versions of the original SVM algorithm that help to reduce the false positive errors (Kolz, 2001, Valentini, 2004 & Kittler, 1998). In particular, it has been suggested that combining non-optimal classifiers can help to reduce particularly the variance of the predictor (Valentini, 2004 & Kittler, 1998) and consequently the misclassification errors. In order to achieve this goal, different versions of the classifier are usually built by sampling the patterns or the features (Breiman, 1996). However, in our application it is expected that the aggregation of strong classifiers will help to reduce more the false positive errors (Provost, 2001 & Hershop, 2005). In this paper, we address the problem of reducing the false positive errors by combining classifiers based on multiple dissimilarities. To this aim, a diversity of classifiers is built considering dissimilarities that reflect different features of the data. The dissimilarities are first embedded into an Euclidean space where a SVM is adjusted for each measure. Next, the classifiers are aggregated using a voting strategy (Kittler, 1998). The method proposed has been applied to the Spam UCI machine learning database (Hastie, 2001) with remarkable results.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124534665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Bio-Inspired Algorithms in Bioinformatics II 生物信息学中的生物启发算法2

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.ch038

José Antonio Seoane Fernández, Mónica Miguélez Rico

引用次数: 0

Adaptive Neural Algorithms for PCA and ICA PCA和ICA的自适应神经算法

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH004

R. Mutihac

{"title":"Adaptive Neural Algorithms for PCA and ICA","authors":"R. Mutihac","doi":"10.4018/978-1-59904-849-9.CH004","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH004","url":null,"abstract":"Artificial neural networks (ANNs) (McCulloch & Pitts, 1943) (Haykin, 1999) were developed as models of their biological counterparts aiming to emulate the real neural systems and mimic the structural organization and function of the human brain. Their applications were based on the ability of self-designing to solve a problem by learning the solution from data. A comparative study of neural implementations running principal component analysis (PCA) and independent component analysis (ICA) was carried out. Artificially generated data additively corrupted with white noise in order to enforce randomness were employed to critically evaluate and assess the reliability of data projections. Analysis in both time and frequency domains showed the superiority of the estimated independent components (ICs) relative to principal components (PCs) in faithful retrieval of the genuine (latent) source signals. Neural computation belongs to information processing dealing with adaptive, parallel, and distributed (localized) signal processing. In data analysis, a common task consists in finding an adequate subspace of multivariate data for subsequent processing and interpretation. Linear transforms are frequently employed in data model selection due to their computational and conceptual simplicity. Some common linear transforms are PCA, factor analysis (FA), projection pursuit (PP), and, more recently, ICA (Comon, 1994). The latter emerged as an extension of nonlinear PCA (Hotelling, 1993) and developed in the context of blind source separation (BSS) (Cardoso, 1998) in signal and array processing. ICA is also related to recent theories of the visual brain (Barlow, 1991), which assume that consecutive processing steps lead to a progressive reduction in the redundancy of representation (Olshausen and Field, 1996). This contribution is an overview of the PCA and ICA neuromorphic architectures and their associated algorithmic implementations increasingly used as exploratory techniques. The discussion is conducted on artificially generated suband super-Gaussian source signals. BACKGROUND","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130774027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Nonlinear Techniques for Signals Characterization 信号表征的非线性技术

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH185

J. B. Alonso, P. H. Rodríguez

{"title":"Nonlinear Techniques for Signals Characterization","authors":"J. B. Alonso, P. H. Rodríguez","doi":"10.4018/978-1-59904-849-9.CH185","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH185","url":null,"abstract":"The field of nonlinear signal characterization and nonlinear signal processing has attracted a growing number of researchers in the past three decades. This comes from the fact that linear techniques have some limitations in certain areas of signal processing. Numerous nonlinear techniques have been introduced to complement the classical linear methods and as an alternative when the assumption of linearity is inappropriate. Two of these techniques are higher order statistics (HOS) and nonlinear dynamics theory (chaos). They have been widely applied to time series characterization and analysis in several fields, especially in biomedical signals. Both HOS and chaos techniques have had a similar evolution. They were first studied around 1900: the method of moments (related to HOS) was developed by Pearson and in 1890 Henri Poincare found sensitive dependence on initial conditions (a symptom of chaos) in a particular case of the three-body problem. Both approaches were replaced by linear techniques until around 1960, when Lorenz rediscovered by coincidence a chaotic system while he was studying the behaviour of air masses. Meanwhile, a group of statisticians at the University of California began to explore the use of HOS techniques again. However, these techniques were ignored until 1980 when Mendel (Mendel, 1991) developed system identification techniques based on HOS and Ruelle (Ruelle, 1979), Packard (Packard, 1980), Takens (Takens, 1981) and Casdagli (Casdagli, 1989) set the methods to model nonlinear time series through chaos theory. But it is only recently that the application of HOS and chaos in time series has been feasible thanks to higher computation capacity of computers and Digital Signal Processing (DSP) technology. The present article presents the state of the art of two nonlinear techniques applied to time series analysis: higher order statistics and chaos theory. Some measurements based on HOS and chaos techniques will be described and the way in which these measurements characterize different behaviours of a signal will be analized. The application of nonlinear measurements permits more realistic characterization of signals and therefore it is an advance in automatic systems development.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131106991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Facial Expression Recognition for HCI Applications 面向人机交互应用的面部表情识别

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH095

F. Dornaika, B. Raducanu

引用次数: 20

Morphological Filtering Principles 形态滤波原理

Encyclopedia of Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.4018/978-1-59904-849-9.CH162

J. Crespo

引用次数: 0