Titlebook: Data Mining and Computational Intelligence; Abraham Kandel,Mark Last,Horst Bunke Book 2001 Physica-Verlag Heidelberg 2001 computational in

ectropion

Intelligent Mining in Image Databases, with Applications to Satellite Imaging and to Web Search,otos, a large amount of the information stored on the Web is in the form of images, etc. It is therefore desirable to use this image information in data mining. Unfortunately, most existing data mining techniques have been designed for mining numerical data and are thus not well suited for image dat

使隔离

Fuzzy Genetic Modeling and Forecasting for Nonlinear Time Series,eory as well as natural selection, and hence is called “genetic modeling”. In order to find a predictive model from the nonlinear time series, we make use of ‘survival of the fittest’ principle of evolution. Through the process of genetic evolution, the AIC criteria are used as the performance measu

慢慢流出

Hyaluronic-Acid

矿石

Data Mining and Computational Intelligence978-3-7908-1825-3Series ISSN 1434-9922 Series E-ISSN 1860-0808

cinder

Post-Inflammatory Hyperpigmentationata mining. Links between our approach to linguistic summaries and the well-known technique of association rules is shown. The generation of linguistic summaries is implemented by using the authors’ FQUERY for Access package.

愚蠢人

Dale S. Huff MD,Dale S. Huff MDapplication areas, however, data is more complicated: real-life data is often obtained as an image from a camera rather than a few measurements. Furthermore, this image can also change dynamically. In this paper, we present several examples of how soft computing is related to mining such data.

书法

Fuzzy Linguistic Summaries via Association Rules,ata mining. Links between our approach to linguistic summaries and the well-known technique of association rules is shown. The generation of linguistic summaries is implemented by using the authors’ FQUERY for Access package.

裂缝

压迫

Pierre Russo MD,Pierre Russo MDThis chapter describes a method for knowledge discovery. First, we will describe the general approach to knowledge discovery, its characteristics and components. Then we will describe our method. Finally we will provide an example from the area of “economic modeling” to illustrate the use of soft regression in knowledge discovery.