{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T04:08:05Z","timestamp":1706069285717},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2005,3]]},"abstract":"<jats:p>In the neural network literature, many preprocessing techniques, such as feature de-correlation, input unbiasing and normalization, are suggested to accelerate multilayer perceptron training. In this paper, we show that a network trained with an original data set and one trained with a linear transformation of the original data will go through the same training dynamics, as long as they start from equivalent states. Thus preprocessing techniques may not be helpful and are merely equivalent to using a different weight set to initialize the network. Theoretical analyses of such preprocessing approaches are given for conjugate gradient, back propagation and the Newton method. In addition, an efficient Newton-like training algorithm is proposed for hidden layer training. Experiments on various data sets confirm the theoretical analyses and verify the improvement of the new algorithm.<\/jats:p>","DOI":"10.1142\/s0218001405004022","type":"journal-article","created":{"date-parts":[[2005,3,17]],"date-time":"2005-03-17T11:46:57Z","timestamp":1111060017000},"page":"217-247","source":"Crossref","is-referenced-by-count":6,"title":["EFFECTS OF NONSINGULAR PREPROCESSING ON FEEDFORWARD NETWORK TRAINING"],"prefix":"10.1142","volume":"19","author":[{"given":"CHANGHUA","family":"YU","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MICHAEL T.","family":"MANRY","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JIANG","family":"LI","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.3.450"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.2.141"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.4.494"},{"key":"rf4","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Networks for Pattern Recognition","author":"Bishop C.","year":"1995"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(98)00109-X"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1007\/BF02551274"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1080\/02757259309532163"},{"key":"rf9","volume-title":"Practical Methods of Optimization","author":"Fletcher R.","year":"1987"},{"key":"rf10","volume-title":"Introduction to Statistical Pattern Recognition","author":"Fukunaga K.","year":"1990"},{"key":"rf12","first-page":"356","volume":"2","author":"Fung A. 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