Titlebook: Computational Intelligence Paradigms; Innovative Applicati Lakhmi C. Jain,Mika Sato-Ilic,Canicious Abeynayake Book 2008 Springer-Verlag Ber

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A Quest for Adaptable and Interpretable Architectures of Computational Intelligence,n efficient manner) and interpretable (so that the results are easily understood by the user or designer). The logic is the language of interpretable constructs. Neural architectures offer a flexible and convenient setting for learning. The study conveys a message that a suitable combination of logi

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MembershipMap: A Data Transformation for Knowledge Discovery Based on Granulation and Fuzzy Memberslled MembershipMap, strives to granulate and extract the underlying sub-concepts of each raw attribute. The orthogonal union of these sub-concepts are then used to define a new membership space. The sub-concept soft labels of each point in the original space determine the position of that point in t

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Ensemble MLP Classifier Design,d for the number of free parameters. Most commonly, parameters are set with the help of either a validation set or cross-validation techniques, but there is no guarantee that a pseudo-test set is representative. Further difficulties with MLPs include long training times and local minima. In this cha

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Functional Principal Points and Functional Cluster Analysis,ts which minimizes the sum of expected squared distances from every points in the distribution to the nearest points of the set, and are mathematically equivalent to centers of gravity by .-means clustering [3]. The concept of principal points can be extended for functional data analysis [16]. We ca

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Cluster Validating Techniques in the Presence of Duplicates,rom multiple databases or for some other reasons is an important line of research. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of Silhouette width, Calinski & Harbasz index (pseudo F-statistics) and Baker & Hubert index (. index) algorithms for exact and a

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Support Vector Machines and Features for Environment Perception in Mobile Robotics,ful information to navigate safely. In this sense, classification techniques applied to sensor data may enhance the way mobile robots sense their surroundings. Amongst several techniques to classify data and to extract relevant information from the environment, Support Vector Machines (SVM) have dem