Che-Chern Lin

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In this paper, we use decision trees to establish the decision models for insurance purchases. Five major types of insurances are involved in this study including life, annuity, health, accident, and investment-oriented insurances. Four decision tree methods were used to build the decision models including Chi-square Automatic Interaction Detector (CHAID),(More)
In this paper, we use feed forward neural networks with the back-propagation algorithm to build decision models for five insurances including life, annuity, health, accident, and investment-oriented insurances. Six features (variables) were selected for the inputs of the neural networks including age, sex, annual income, educational level, occupation, and(More)
In this paper, the authors propose a schema for a decision support system for basketball defense strategies using a fuzzy expert system. The authors introduce the original ideas for the proposed schema and the background of the basketball defense strategies. The authors also present the fuzzy input and output attributes for the fuzzy expert system according(More)
A constructive algorithm to implement convex recursive deletion regions via two-layer perceptrons has been presented in a recent study. In the algorithm, the absolute values of the weights become larger and larger when the number of nested layers of a convex recursive deletion region increases. In addition, the absolute values of the weights are determined(More)
This paper designed a interactive loop for triggering knowledge transfer. This paper tests the ability of two technology behavior theory model – the theory of reasoned action and the theory of planned behavior-in predicting on-line knowledge-transfer intention. In addition, a comparison of the two theories is conducted. Data were collected from web-based(More)
This is a series of studies to discuss the partitioning capabilities of multi-layer perceptrons on dis-jointly removed non-convex (DJRNC) decision regions. There are two papers proposed in the series of studies including part A and part B. In part A, we propose a network structure to implement DJRNC decision regions using multi-layer perceptrons. In the(More)