다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining - Concepts and Techniques.

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

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A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

International Joint Conference on Neural Networks

A comprehensive survey on support vector machine classification: Applications, challenges and trends

In general, fuzzy neural networks cannot match nonlinear systems exactly. Unmodeled dynamic can lead parameters drive and even instability problem. Some robust modifications must be contained, in order to guarantee Lyapunov stability. In this paper input-to-state stability is applied to access robust training algorithm of the fuzzy neural networks. We state that the normal gradient descent law with a time-varying learning rate is stable in the sense of L/sub /spl infin//. The fuzzy neural networks approximation, which is suggested in this paper, needs no robust modification and is robust to any bounded uncertainty.

Fuzzy neural modeling using stable learning algorithm

Border points are those instances located at the outer margin of dense clusters of samples. The detection is important in many areas such as data mining, image processing, robotics, geographic information systems and pattern recognition. In this paper we propose a novel method to detect border samples. The proposed method makes use of a discretization and works on partitions of the set of points. Then the border samples are detected by applying an algorithm similar to the presented in reference [8] on the sides of convex hulls. We apply the novel algorithm on classification task of data mining; experimental results show the effectiveness of our method.

/pdf/border-samples-detection-for-data-mining-applications-using-1mv03clzs0.pdf

Border samples detection for data mining applications using non convex hulls

Support vector machines (SVM) for binary classification have been developed in a broad field of applications. But normal SVM algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel two-stage SVM classification approach for large data sets: minimum enclosing ball (MEB) clustering is introduced to select the training data from the original data set for the first stage SVM, and a de-clustering technique is then proposed to recover the training data for the second stage SVM. Then we extend binary SVM classification to case of multiclass. The novel two-stage multi-class SVM has distinctive advantages on dealing with huge data sets. Finally, we apply the proposed method on several benchmark problems, experimental results demonstrate that our approach have good classification accuracy while the training is significantly faster than other SVM classifiers.

Multi-Class Support Vector Machines for Large Data Sets via Minimum Enclosing Ball Clustering

Support vector machine (SVM) is not suitable for classification on large data sets due to its training complexity. Convex hull can simplify SVM training, however the classification accuracy becomes lower when there are inseparable points. This paper introduces a novel method for SVM classification, called convex-concave hull. After a grid processing, the convex hull is used to find extreme points. Then we detect a concave (non-convex) hull, the vertices of it are used to train SVM. We applied the proposed method on several problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other state of the art methods.

Convex-Concave Hull for Classification with Support Vector Machine

Neural networks are a widely used data mining technique, but for high-dimensional datasets, the training process of the normal neural networks, such as multilayer perceptron (MLP), is very slow. It is an important drawback for using them in real-time data mining applications where the main requirement is to have an answer within a short time. In this research work we propose a CMAC neural network adaptation for data mining, which most provides fast training time and guaranteed convergence. This paper describes how we built a CMAC adaptation for data mining, obtaining a classification model that can be applied to real-life datasets. Experimental results show that CMAC may be an alternative model for high-dimensional data classification in data mining

Xiaoou Li

Papers

Fuzzy neural modeling using stable learning algorithm

Border samples detection for data mining applications using non convex hulls

Multi-Class Support Vector Machines for Large Data Sets via Minimum Enclosing Ball Clustering

Convex-Concave Hull for Classification with Support Vector Machine

Data Mining based on CMAC Neural Networks