Abstract: There is provided a method including, acquiring brain function data and diffusion tensor data, calculating a connection degree between voxels adjacent to each other based on the diffusion tensor data, constituting a data evaluation value based on the brain function data and the connection degree between the adjacent voxels, subjecting the data evaluation value to nonparametric regression analysis, and forming and displaying images based on results of the analysis.

Abstract: There is provided a method including, acquiring brain function data and diffusion tensor data (S10), calculating a connection degree between voxels adjacent to each other based on the diffusion tensor data (S30), constituting a data evaluation value based on the brain function data and the connection degree between the adjacent voxels (S40), subjecting the data evaluation value to nonparametric regression analysis (S50), and forming and displaying images based on results of the analysis (S60, S70).

Abstract: A neural network analysis apparatus has an input means 1 for inputting multilinear functions that represent the various units of a trained neural network, a storage means 2 therefor, term generators 3 that successively generate terms of a Boolean function used for approximation from the coefficients of each term of the multilinear function input for each unit, a judgment condition storage section 22 into which is stored the conditions for generating each term of the Boolean function to be used in the approximation, an apparatus 5 that links the generated Boolean function terms, and a Boolean function synthesizing apparatus 6 for representing the output unit by input variables. The term generators 3 each have four sub-apparatuses: a data-limiting apparatus 31, a minimum value calculation apparatus 32, a non-important, attribute processing apparatus 33, and a function judgment apparatus 34.

Abstract: An inference apparatus predicts the class of a new instance whose class is unknown or generates a rule which is common to an instance set. In the inference apparatus, an instance memory section stores the instance set which each include attribute--value and class. The data type of the attribute--value is discrete type or continuous type. An instance expression section converts the attribute--value of discrete type to a first data expression and the attribute--value of continuous type to a second data expression. A parameter calculation section generates a linear function of each class in which the attribute--value is a variable and calculates a coefficient of each variable in the linear function by assigning one of the first data expression and the second data expression to the variables.