LEADER 05466nam 22007575 450 001 996465914003316 005 20230803010915.0 010 $a3-540-49430-8 024 7 $a10.1007/3-540-49430-8 035 $a(CKB)1000000000211023 035 $a(SSID)ssj0000325106 035 $a(PQKBManifestationID)11242166 035 $a(PQKBTitleCode)TC0000325106 035 $a(PQKBWorkID)10320762 035 $a(PQKB)11491589 035 $a(DE-He213)978-3-540-49430-0 035 $a(MiAaPQ)EBC3072353 035 $a(PPN)155197304 035 $a(EXLCZ)991000000000211023 100 $a20121227d1998 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aNeural Networks: Tricks of the Trade$b[electronic resource] /$fedited by Genevieve B. Orr, Klaus-Robert Müller 205 $a1st ed. 1998. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1998. 215 $a1 online resource (VIII, 432 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v1524 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-65311-2 320 $aIncludes bibliographical references and indexes. 327 $aSpeeding Learning -- Efficient BackProp -- Regularization Techniques to Improve Generalization -- Early Stopping - But When? -- A Simple Trick for Estimating the Weight Decay Parameter -- Controlling the hyperparameter search in MacKay?s Bayesian neural network framework -- Adaptive Regularization in Neural Network Modeling -- Large Ensemble Averaging -- Improving Network Models and Algorithmic Tricks -- Square Unit Augmented Radially Extended Multilayer Perceptrons -- A Dozen Tricks with Multitask Learning -- Solving the Ill-Conditioning in Neural Network Learning -- Centering Neural Network Gradient Factors -- Avoiding roundoff error in backpropagating derivatives -- Representing and Incorporating Prior Knowledge in Neural Network Training -- Transformation Invariance in Pattern Recognition ? Tangent Distance and Tangent Propagation -- Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton -- Neural Network Classification and Prior Class Probabilities -- Applying Divide and Conquer to Large Scale Pattern Recognition Tasks -- Tricks for Time Series -- Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions -- How to Train Neural Networks. 330 $aIt is our belief that researchers and practitioners acquire, through experience and word-of-mouth, techniques and heuristics that help them successfully apply neural networks to di cult real world problems. Often these \tricks" are theo- tically well motivated. Sometimes they are the result of trial and error. However, their most common link is that they are usually hidden in people?s heads or in the back pages of space-constrained conference papers. As a result newcomers to the eld waste much time wondering why their networks train so slowly and perform so poorly. This book is an outgrowth of a 1996 NIPS workshop called Tricks of the Trade whose goal was to begin the process of gathering and documenting these tricks. The interest that the workshop generated motivated us to expand our collection and compile it into this book. Although we have no doubt that there are many tricks we have missed, we hope that what we have included will prove to be useful, particularly to those who are relatively new to the eld. Each chapter contains one or more tricks presented by a given author (or authors). We have attempted to group related chapters into sections, though we recognize that the di erent sections are far from disjoint. Some of the chapters (e.g., 1, 13, 17) contain entire systems of tricks that are far more general than the category they have been placed in. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v1524 606 $aComputers 606 $aArtificial intelligence 606 $aMicroprocessors 606 $aPattern recognition 606 $aComputational complexity 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProcessor Architectures$3https://scigraph.springernature.com/ontologies/product-market-codes/I13014 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022 615 0$aComputers. 615 0$aArtificial intelligence. 615 0$aMicroprocessors. 615 0$aPattern recognition. 615 0$aComputational complexity. 615 14$aComputation by Abstract Devices. 615 24$aArtificial Intelligence. 615 24$aProcessor Architectures. 615 24$aPattern Recognition. 615 24$aComplexity. 676 $a006.3/2 702 $aOrr$b Genevieve$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMüller$b Klaus-Robert$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465914003316 996 $aNeural Networks: Tricks of the Trade$91944994 997 $aUNISA