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A B+ tree index for a full-text search through documents is created fast and fast searches are implemented using the index. A B+ tree index to register sets of words serving as keys and documents containing the words is constituted of a plurality of B+ tree subindexes; a document identification numb

A B+ tree index for a full-text search through documents is created fast and fast searches are implemented using the index. A B+ tree index to register sets of words serving as keys and documents containing the words is constituted of a plurality of B+ tree subindexes; a document identification number id and a word identification number iw are assigned to a document and a word to uniquely identify them; as a function to apply to documents, a hash function Hd is provided that maps a document identification number to a value indicating the position of horizontal direction of a two-dimensional array, and as a function to apply to words, a hash function Hw is provided that maps a word identification number to a value indicating the position of vertical direction of the two-dimensional array; and the occurrence of a word in a document is registered in a corresponding subindex B+ tree (Hd(id), Hw(iw)) by using values obtained by applying the hash functions to the document identification number and the word identification number, respectively. The index is searched using a value with a word identification number as a key and a document identification number concatenated.

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A B+ tree index for a full-text search through documents is created fast and fast searches are implemented using the index. A B+ tree index to register sets of words serving as keys and documents containing the words is constituted of a plurality of B+ tree subindexes; a document identification numb

A B+ tree index for a full-text search through documents is created fast and fast searches are implemented using the index. A B+ tree index to register sets of words serving as keys and documents containing the words is constituted of a plurality of B+ tree subindexes; a document identification number id and a word identification number iw are assigned to a document and a word to uniquely identify them; as a function to apply to documents, a hash function Hd is provided that maps a document identification number to a value indicating the position of horizontal direction of a two-dimensional array, and as a function to apply to words, a hash function Hw is provided that maps a word identification number to a value indicating the position of vertical direction of the two-dimensional array; and the occurrence of a word in a document is registered in a corresponding subindex B+ tree (Hd(id), Hw(iw)) by using values obtained by applying the hash functions to the document identification number and the word identification number, respectively. The index is searched using a value with a word identification number as a key and a document identification number concatenated. on Dec. 8, 1996). "Neural informatics pearls of wisdom", (available on http://www.-smi.stanford.edu/people/ . . . hysiology/Neuro_Pearls.html#ANN-app on Nov. 21, 1996). Neural Networks & intelligent systems newsletter, Derwent Direct, Issue 3, (Aug., 1996). Logical Designs Consulting, Inc., "Thinks� and ThinksPro� Neural networks for windows: Your complete neural network development environment". Moneta et al., Automated diagnosis and disease characterization using neural network analysis, Instittute of Electrical and Electronics Engineers--Emergent Innovations on Information Transfer Processing and decision Making, Chicago, vol. 1 of 2: 123-128 (1992). Nejad et al., Significance measures and data dependency in classification methods, Instit. Elect. Electron. Engineers Intl. Conference on Neural Network Proceedings, Australia: 1816-1822 (1995). Utans, et al., "Selecting neural network architectures via the prediction risk: Application to corporate bond rating prediction", Proceedings of the First International Conference on Artificial Intelligence Applications on Wall Street, Washington D.C., IEEE Computer Society Press. pp. 35-41 (1991). Diller, W., "Horus computer-enhanced diagnostics", In Vivo: The Business and Medicine Report, pp. 3-10, 1997. Burke, Evaluating artificial neural networks for medical applications, International Conference on Neural Networks 4:2494-2495 (1997). El-Deredy et al., Identification of relevant features in /sup 1/H MR tumour spectra using neural networks, Fourth International Conference on Artificial Neural Networks pp. 454-458 (1995). Furundzic et al., Artificial neural networks for early breast carcinoma detection, International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing 355-359 (1996). Gorzalczany, An idea of the application of fuzzy neural networks to medical decision support systems, Proceedings of the IEEE International Symposium on Industrial Electronics 1:398-403 (1996). Kupinski et al., Feature selection and classifiers for the computerized detection of mass lesions in digital mammography, International Conference on Neural Networks 4:2460-2463 (1997). Micheli-Tzanakou et al., Myocardial infarction: diagnosis and vital status prediction using neural networks, Computers in Cardiology pp. 229-232 (1993). Rachid et al., Segmentation of sputum color image for lung cancer diagnosis Internat