Non-linear model automatic generating method
First Claim
1. A method of automatically generating a non-linear model for generating a non-linear model of a neural network, comprising steps of:
- selecting characteristics of learning data to be used for learning a non-linear model as a group-by rule through a group-by rule induction processing;
picking up input-output variables from the selected rule; and
generating a non-linear model of m inputs and an n output (m≧
2, n≧
1) by using the picked-up input-output variables.
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Accused Products
Abstract
The present invention is intended to automatically select least required input items for a non-linear model and improve an efficiency of building up the non-linear model. For building up, for example, a neural network as the non-linear model, a group-by rule 105 and dividing point information 106 of the data are automatically generated by a group-by rule induction device 104 by selecting a dividing method option 101 and a group selection information 103 if data for learning 101 is given. An initial neural network model generating device 107 automatically generates an initial neural network model 108 from the group-by rule 105. The initial neural network model is learned in a neural network model learning device 111 and outputted as a post-learning neural network model 112. Data for learning with group information 110 which is the data for learning is generated in a data classification device 109 by using data for learning 102, group-by rule 105 and dividing point information 106. Input-output variables can be automatically selected from the data for learning according to selection of the group and a neural network model of respective groups can be built up.
31 Citations
17 Claims
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1. A method of automatically generating a non-linear model for generating a non-linear model of a neural network, comprising steps of:
-
selecting characteristics of learning data to be used for learning a non-linear model as a group-by rule through a group-by rule induction processing; picking up input-output variables from the selected rule; and generating a non-linear model of m inputs and an n output (m≧
2, n≧
1) by using the picked-up input-output variables.
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2. A method of automatically generating a neural network model for generating a neural network model, comprising steps of:
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selecting characteristics of learning data to be used for learning a neural network model as a group-by rule through a group-by rule induction processing; picking up input-output variables from the selected rule; generating an initial neural network model for determining a structure of the neural network model by using the picked-up input-output variables; and generating a neural network model by learning the initial neural network model according to said learning data.
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3. A method of automatically generating a neural network model for generating a neural network model, comprising steps of:
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inducing characteristics of learning data to be used for learning said neural network model as a group-by rule through a group-by rule induction processing; generating an initial neural network model for determining a structure of the neural network model by the input-output variables, which are picked up from said group-by rule; generating learning data with group information by classifying said learning data for respective groups; and learning said initial neural network model according to the classified learning data. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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Specification