{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T20:10:16Z","timestamp":1737231016770,"version":"3.33.0"},"reference-count":16,"publisher":"Wiley","issue":"13","license":[{"start":{"date-parts":[[2007,3,21]],"date-time":"2007-03-21T00:00:00Z","timestamp":1174435200000},"content-version":"vor","delay-in-days":4462,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems &amp;amp; Computers in Japan"],"published-print":{"date-parts":[[1995,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper presents a neural network algorithm called SOLAR (Supervised One\u2010shot Learning Algorithm for Real\u2010valued inputs) that can complete learning by a single presentation of the real\u2010valued inputs.<\/jats:p><jats:p>The similarity matrix is introduced in SOLAR, which can measure the Euclidean distance between training instances represented by a real number. Based on the geometrical structure of the similarity matrix, the structure of the network, the connection weights, the learning parameters and the linear discriminant function are determined. Then, the learning is executed by a single presentation of the training set. The nonlinearly separable case can be reduced to the linearly separable case by decomposing the set of supervisor signals into linearly separable sets.<\/jats:p><jats:p>The main contribution of this paper is that SOLAR realizes a high\u2010speed learning by a single presentation of the input data composed of real values and a large\u2010scale complex set of data can be learned. This has been difficult in previous models.<\/jats:p><jats:p>In addition, since the number of hidden units and the parameters are determined by the algorithm, there is no problem such as trial\u2010and error.<\/jats:p><jats:p>Simulations show the effectiveness of the proposed method by learning a relatively large\u2010scale problem, such as the backpropagation algorithm, which has been difficult based on the gradient descent method.<\/jats:p>","DOI":"10.1002\/scj.4690261310","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T03:55:53Z","timestamp":1183866953000},"page":"93-104","source":"Crossref","is-referenced-by-count":0,"title":["Supervised one\u2010shot learning algorithm for real\u2010valued inputs"],"prefix":"10.1002","volume":"26","author":[{"given":"Kosei","family":"Demura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuichiro","family":"Anzai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Kajiura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2007,3,21]]},"reference":[{"volume-title":"Parallel Computational Geometry","year":"1993","author":"Akl S. 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