LEADER 05433nam 2200673 450 001 9910141724303321 005 20220504182624.0 010 $a1-118-59550-5 010 $a1-118-59627-7 035 $a(CKB)2560000000148550 035 $a(EBL)1676373 035 $a(SSID)ssj0001181935 035 $a(PQKBManifestationID)11639809 035 $a(PQKBTitleCode)TC0001181935 035 $a(PQKBWorkID)11146655 035 $a(PQKB)10450716 035 $a(OCoLC)866563831 035 $a(MiAaPQ)EBC1676373 035 $a(Au-PeEL)EBL1676373 035 $a(CaPaEBR)ebr10862614 035 $a(CaONFJC)MIL599751 035 $a(PPN)191805270 035 $a(EXLCZ)992560000000148550 100 $a20140512h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aWavelet neural networks $ewith applications in financial engineering, chaos, and classification /$fAntonis K. Alexandridis, Achilleas D. Zapranis 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons,$d2014. 210 4$dİ2014 215 $a1 online resource (263 p.) 300 $aIncludes index. 311 $a1-306-68500-1 311 $a1-118-59252-2 327 $aWavelet Neural Networks; Contents; Preface; 1 Machine Learning and Financial Engineering; Financial Engineering; Financial Engineering and Related Research Areas; Functions of Financial Engineering; Applications of Machine Learning in Finance; From Neural to Wavelet Networks; Wavelet Analysis; Extending the Fourier Transform: The Wavelet Analysis Paradigm; Neural Networks; Wavelet Neural Networks; Applications of Wavelet Neural Networks in Financial Engineering, Chaos, and Classification; Building Wavelet Networks; Variable Selection; Model Selection; Model Adequacy Testing; Book Outline 327 $aReferences2 Neural Networks; Parallel Processing; Processing Units; Activation Status and Activation Rules; Connectivity Model; Perceptron; The Approximation Theorem; The Delta Rule; Backpropagation Neural Networks; Multilayer Feedforward Networks; The Generalized Delta Rule; Backpropagation in practice; Training with Backpropagation; Network Paralysis; Local Minima; Nonunique Solutions; Configuration Reference; Conclusions; References; 3 Wavelet Neural Networks; Wavelet Neural Networks for Multivariate Process Modeling; Structure of a Wavelet Neural Network 327 $aInitialization of the Parameters of the Wavelet NetworkTraining a Wavelet Network with Backpropagation; Stopping Conditions for Training; Evaluating the Initialization Methods; Conclusions; References; 4 Model Selection: Selecting the Architecture of the Network; The Usual Practice; Early Stopping; Regularization; Pruning; Minimum Prediction Risk; Estimating the Prediction Risk Using Information Criteria; Estimating the Prediction Risk Using Sampling Techniques; Bootstrapping; Cross-Validation; Model Selection Without Training; Evaluating the Model Selection Algorithm 327 $aCase 1: Sinusoid and Noise with Decreasing VarianceCase 2: Sum of Sinusoids and Cauchy Noise; Adaptive Networks and Online synthesis; Conclusions; References; 5 Variable Selection: Determining the Explanatory Variables; Existing Algorithms; Sensitivity Criteria; Model Fitness Criteria; Algorithm for Selecting the Significant Variables; Resampling Methods for the Estimation of Empirical Distributions; Evaluating the Variable Significance Criteria; Case 1: Sinusoid and Noise with Decreasing Variance; Case 2: Sum of Sinusoids and Cauchy Noise; Conclusions; References 327 $a6 Model Adequacy: Determining a Networks Future PerformanceTesting the residuals; Testing for Serial Correlation in the Residuals; Evaluation criteria for the prediction ability of the wavelet network; Measuring the Accuracy of the Predictions; Scatter Plots; Linear Regression Between Forecasts and Targets; Measuring the Ability to Predict the Change in Direction; Two simulated Cases; Case 1: Sinusoid and Noise with Decreasing Variance; Case 2: Sum of Sinusoids and Cauchy Noise; Classification; Assumptions and Objectives of Discriminant Analysis; Validation of the Discriminant Function 327 $aEvaluating the Classification Ability of a Wavelet Network 330 $aThrough extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is id 606 $aWavelets (Mathematics) 606 $aNeural networks (Computer science) 606 $aFinancial engineering 615 0$aWavelets (Mathematics) 615 0$aNeural networks (Computer science) 615 0$aFinancial engineering. 676 $a006.3/2 700 $aAlexandridis$b Antonis K.$0959336 702 $aZapranis$b Achilleas$f1965- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141724303321 996 $aWavelet neural networks$92173747 997 $aUNINA