Titlebook: Learning, Networks and Statistics; Giacomo Riccia,Hans-Joachim Lenz,Rudolf Kruse Conference proceedings 1997 Springer-Verlag Wien 1997 alg

Indurate

Overtraining in Single-Layer Perceptronsperceptron training are the growth of magnitude of weights with an increase in number of iterations and, as a consequence, a change of the criterion used to find the weights. Therefore in non-linear SLP training one can obtain various statistical classification rules of different complexity and, the

MOAT

Adaptive Market Simulation and Risk Assessment market and company structures (demand and supply side, prices, values, resources, budgets, ...), dynamics (growth, structural changes, ...), interdependencies (e. g. between market and company related factors). The appoach provides the following features to end users:

航海太平洋

Processing of Prior-Information in Statistics by Projections on Convex Conesite matrix. It is shown that this algorithm converges almost surely. The power of the algorithm is demonstrated by examples from statistics in which processing of prior information may be formulated as projections of parametervectors on polyhedral cones.

Manifest

oxidize

Data Analysis in Industryt of the data: (1) Preprocessing the data, (2) Reducing the data, (3) Modeling the data, (4) Rule extraction, and (5) Drift analysis. We also show some examples of industrial applications where fuzzy logic based data analysis plays a role.

Corral

Fuzzy Shell Cluster Analysislusters, i.e. clusters that lie in nonlinear subspaces. Therefore we introduce the main principles of fuzzy cluster analysis first. In the following we present some fuzzy shell clustering algorithms In many applications it is necessary to determine the number of clusters as well as the classificatio

憤慨點吧

會犯錯誤

From the Art of KDD to the Science of KDDing to invest into it. This fact demonstrates the excellent social value of KDD. A Science, however, is not uniquely defined by this feature. It needs also to show an internal logic, due to a specific approach to the real-life problems it deals with. This last point of view has been less emphasized

Madrigal

FAST

Preprocessing by a Cost-Sensitive Literal Reduction Algorithm: Reduce solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of l