Discriminative learning for speech recognition theory and practice / by He, Xiaodong, 1973-, Synthesis digital library of engineering and computer science

Table of Contents: “…Introduction and background -- What is discriminative learning? -- What is speech recognition? …”
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Computer vision : statistical models for Marr's paradigm / by Zhu, Song Chun, Wu, Ying Nian, SpringerLink (Online service)

Table of Contents: “…Preface -- About the Authors -- 1 Introduction -- 2 Statistics of Natural Images -- 3 Textures -- 4 Textons -- 5 Gestalt Laws and Perceptual Organizations -- 6 Primal Sketch: Integrating Textures and Textons -- 7 2.1D Sketch and Layered Representation -- 8 2.5D Sketch and Depth Maps -- 9 Learning about information Projection -- 10 Informing Scaling and Regimes of Models -- 11 Deep Images and Models -- 12 A Tale of Three Families: Discriminative, Generative and Descriptive Models -- Bibliography.…”
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Elements of dimensionality reduction and manifold learning / by Ghojogh, Benyamin, Crowley, Mark, Karray, Fakhri, Ghodsi, Ali, SpringerLink (Online service)

Table of Contents: “…Chapter 1: Introduction -- Part 1: Preliminaries and Background -- Chapter 2: Background on Linear Algebra -- Chapter 3: Background on Kernels -- Chapter 4: Background on Optimization -- Part 2: Spectral dimensionality Reduction -- Chapter 5: Principal Component Analysis -- Chapter 6: Fisher Discriminant Analysis -- Chapter 7: Multidimensional Scaling, Sammon Mapping, and Isomap -- Chapter 8: Locally Linear Embedding -- Chapter 9: Laplacian-based Dimensionality Reduction -- Chapter 10: Unified Spectral Framework and Maximum Variance Unfolding -- Chapter 11: Spectral Metric Learning -- Part 3: Probabilistic Dimensionality Reduction -- Chapter 12: Factor Analysis and Probabilistic Principal Component Analysis -- Chapter 13: Probabilistic Metric Learning -- Chapter 14: Random Projection -- Chapter 15: Sufficient Dimension Reduction and Kernel Dimension Reduction -- Chapter 16: Stochastic Neighbour Embedding -- Chapter 17: Uniform Manifold Approximation and Projection (UMAP) -- Part 4: Neural Network-based Dimensionality Reduction -- Chapter 18: Restricted Boltzmann Machine and Deep Belief Network -- Chapter 19: Deep Metric Learning -- Chapter 20: Variational Autoencoders -- Chapter 21: Adversarial Autoencoders.…”
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Multivariate statistical methods in quality management / by Yang, Kai, AccessEngineering

Table of Contents: “…Cover -- Contents -- Preface -- Chapter 146; Multivariate Statistical Methods and Quality -- 146;1 Overview of Multivariate Statistical Methods -- 146;146;1 Graphical multivariate data display and data stratification -- 146;146;2 Multivariate normal distribution and multivariate sampling distribution -- 146;146;3 Multivariate analysis of variance -- 146;146;4 Principal component analysis and factor analysis -- 146;146;5 Discriminant analysis -- 146;146;6 Cluster analysis -- 146;146;7 Mahalanobis Taguchi system 40;MTS41; -- 146;146;8 Path analysis and structural model -- 146;146;9 Multivariate process control -- 146;2 Applications of Multivariate Statistical Methods in Business and Industry -- 146;246;1 Data mining -- 146;246;2 Chemometrics -- 146;246;3 Other applications -- 146;3 Overview of Quality Assurance and Possible Roles of Multivariate Statistical Methods -- 146;346;1 Stage 058; Impetus47;ideation -- 146;346;2 Stage 158; Customer and business requirements study -- 146;346;3 Stage 258; Concept development -- 146;346;4 Stage 358; Product47;service design47;prototyping -- 146;346;5 Stage 458; Manufacturing process preparation47;product launch -- 146;346;6 Stage 558; Production -- 146;346;7 Stage 658; Product47;service consumption -- 146;346;8 Stage 758; Disposal -- 146;4 Overview of Six Sigma and Possible Roles of Multivariate Statistical Methods -- 146;446;1 Stage 158; Define the project and customer requirements 40;D or define step41; -- 146;446;2 Stage 258; Measuring process performance -- 146;446;3 Stage 358; Analyze data and discover causes of the problem -- 146;446;4 Stage 458; Improve the process -- 146;446;5 Stage 558; Control the process -- Chapter 246; Graphical Multivariate Data Display and Data Stratification -- 246;1 Introduction -- 246;2 Graphical Templates for Multivariate Data -- 246;246;1 Charts and graphs -- 246;246;2 Templates for displaying multivariate data -- 246;3 Data Visualization and Animation -- 246;346;1 Introduction to data visualization -- 246;4 Multivariate Data Stratification -- 246;446;1 Multi45;vari chart technique -- 246;446;2 Graphical analysis of multivariate variation pattern -- Chapter 346; Introduction to Multivariate Random Variables44; Normal Distribution44; and Sampling Properties -- 346;1 Overview of Multivariate Random Variables -- 346;2 Multivariate Data Sets and Descriptive Statistics -- 346;246;1 Multivariate data sets -- 346;246;2 Multivariate descriptive statistics -- 346;3 Multivariate Normal Distributions -- 346;346;1 Some properties of the multivariate normal distribution -- 346;4 Multivariate Sampling Distribution -- 346;446;1 Sampling distribution of X -- 346;446;2 Sampling distribution of S -- 346;446;3 Central limit theorem applied to multivariate samples -- 346;446;4 Hotelling8217;s T[sup40;241;] distribution -- 346;446;5 Summary -- 346;5 Multivariate Statistical Inferences on Mean Vectors -- 346;546;1 Small sample multivariate hypothesis testing on a mean vector -- 346;546;2 Large sample multivariate hypothesis testing on a mean vector -- 346;546;3 Small sample multivariate hypothesis testing on the equality of two mean vectors -- 346;546;4 Large sample multivariate hypothesis testing on the equality of two mean vectors -- 346;546;5 Overview of confidence intervals and confidence.…”
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Random processes for image and signal processing / by Dougherty, Edward R., SPIE Digital Library

Table of Contents: “…Optimal filtering -- Optimal mean-square-error filters -- Conditional expectation -- Optimal nonlinear filter -- Optimal filter for jointly normal random variables -- Multiple observation variables -- Bayesian parametric estimation -- Optimal finite-observation linear filters -- Linear filters and the orthogonality principle -- Design of the optimal linear filter -- Optimal linear filter in the jointly Gaussian case -- Role of wide-sense stationarity -- Signal-plus-noise model -- Edge detection -- Steepest descent -- Steepest descent iterative algorithm -- Convergence of the steepest-descent algorithm -- Least-mean-square adaptive algorithm -- Convergence of the LMS algorithm -- Nonstationary processes -- Least-squares estimation -- Pseudoinverse estimator -- Least-squares estimation for nonwhite noise -- Multiple linear regression -- Least-squares image restoration -- Optimal linear estimation of random vectors -- Optimal linear filter for linearly dependent observations -- Optimal estimation of random vectors -- Optimal linear filters for random vectors -- Recursive linear filters -- Recursive generation of direct sums -- Static recursive optimal linear filtering -- Dynamic recursive optimal linear filtering -- Optimal infinite-observation linear filters -- Wiener-Hopf equation -- Wiener filter -- Optimal linear filter in the context of a linear model -- The linear signal model -- Procedure for finding the optimal linear filter -- Additive white noise -- Discrete domains -- Optimal linear filters via canonical expansions -- Integral decomposition into white noise -- Integral equations involving the autocorrelation function -- Solution via discrete canonical expansions -- Optimal binary filters -- Binary conditional expectation -- Boolean functions and optimal translation-invariant filters -- Optimal increasing filters -- Pattern classification -- Optimal classifiers -- Gaussian maximum-likelihood classification -- Linear discriminants -- Neural networks -- Two-layer neural networks -- Steepest descent for nonquadratic error surfaces -- Sum-of-squares error -- Error back-propagation -- Error back-propagation for multiple outputs -- Adaptive network design -- Exercises for chapter 4.…”
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