# krainaksiazek regularization and learning theory 20124155

- znaleziono 5 produktów w 2 sklepach

### Machine learning Books on Demand

**Książki / Literatura obcojęzyczna**

Source: Wikipedia. Pages: 254. Chapters: Artificial neural network, Supervised learning, Hidden Markov model, Pattern recognition, Reinforcement learning, Principal component analysis, Self-organizing map, Overfitting, Cluster analysis, Granular computing, Rough set, Mixture model, Expectation-maximization algorithm, Radial basis function network, Types of artificial neural networks, Learning to rank, Forward-backward algorithm, Perceptron, Category utility, Neural modeling fields, Dominance-based rough set approach, Principle of maximum entropy, Non-negative matrix factorization, Concept learning, K-means clustering, Structure mapping engine, Viterbi algorithm, Cross-validation, Hierarchical temporal memory, Activity recognition, Algorithmic inference, Formal concept analysis, Gradient boosting, Information bottleneck method, Nearest neighbor search, Simultaneous localization and mapping, Markov decision process, Gittins index, K-nearest neighbor algorithm, General Architecture for Text Engineering, Reasoning system, Concept drift, Uniform convergence, Conceptual clustering, Multi-armed bandit, Multilinear subspace learning, Conditional random field, DBSCAN, Feature selection, Learning with errors, Weka, Evolutionary algorithm, Iris flower data set, Binary classification, OPTICS algorithm, Partially observable Markov decision process, Constrained Conditional Models, Group method of data handling, Learning classifier system, Random forest, Statistical classification, Analogical modeling, Bregman divergence, Backpropagation, Temporal difference learning, Loss function, Curse of dimensionality, Alternating decision tree, Evolutionary multi-modal optimization, Stochastic gradient descent, Kernel principal component analysis, Explanation-based learning, K-medoids, RapidMiner, Transduction, Variable-order Markov model, Kernel adaptive filter, Classification in machine learning, Grammar induction, Sense Networks, Glivenko Cantelli theorem, Cross-entropy method, Dimension reduction, Rand index, Spiking neural network, Feature Selection Toolbox, Co-training, Multinomial logit, Computational learning theory, Local Outlier Factor, Q-learning, Gaussian process, Evolvability, Universal Robotics, Crossover, Shattering, Cluster-weighted modeling, Version space, Variable kernel density estimation, Calibration, Randomized weighted majority algorithm, Leabra, Growing self-organizing map, TD-Gammon, Prior knowledge for pattern recognition, Generative topographic map, VC dimension, ID3 algorithm, String kernel, Prefrontal Cortex Basal Ganglia Working Memory, Meta learning, Inductive transfer, Margin classifier, Active learning, Feature extraction, Regularization, IDistance, Dynamic time warping, Latent variable, Layered hidden Markov model, Empirical risk minimization, Jabberwacky, Inductive bias, Shogun, Confusion matrix, Never-Ending Language Learning, Accuracy paradox, FLAME clustering, Smart variables, Probably approximately correct learning, Hierarchical hidden Markov model, Document classification, Adjusted mutual information, Generalization error, Knowledge discovery, Quadratic classifier, Ugly duckling theorem, Bongard problem, Online machine learning, Algorithmic learning theory, Information Fuzzy Networks, Knowledge integration, Bootstrap aggregating, Early stopping, Kernel methods, Bag of words model, CIML community portal, Sequence labeling, Semi-supervised learning, Minimum redundancy feature selection, Matthews correlation coefficient, Learn...

Sklep: Libristo.pl

### Theory of Reproducing Kernels and Applications Springer, Berlin

**Książki / Literatura obcojęzyczna**

This book provides a large extension of the general theory of reproducing kernels published by N. Aronszajn in 1950, with many concrete applications. In Chapter 1, many concrete reproducing kernels are first introduced with detailed information. Chapter 2 presents a general and global theory of reproducing kernels with basic applications in a self-contained way. Many fundamental operations among reproducing kernel Hilbert spaces are dealt with. Chapter 2 is the heart of this book. Chapter 3 is devoted to the Tikhonov regularization using the theory of reproducing kernels with applications to numerical and practical solutions of bounded linear operator equations. In Chapter 4, the numerical real inversion formulas of the Laplace transform are presented by applying the Tikhonov regularization, where the reproducing kernels play a key role in the results. Chapter 5 deals with ordinary differential equations; Chapter 6 includes many concrete results for various fundamental partial differential equations. In Chapter 7, typical integral equations are presented with discretization methods. These chapters are applications of the general theories of Chapter 3 with the purpose of practical and numerical constructions of the solutions. In Chapter 8, hot topics on reproducing kernels are presented; namely, norm inequalities, convolution inequalities, inversion of an arbitrary matrix, representations of inverse mappings, identifications of nonlinear systems, sampling theory, statistical learning theory and membership problems. Relationships among eigen-functions, initial value problems for linear partial differential equations, and reproducing kernels are also presented. Further, new fundamental results on generalized reproducing kernels, generalized delta functions, generalized reproducing kernel Hilbert spaces, and as well, a general integral transform theory are introduced. In three Appendices, the deep theory of Akira Yamada discussing the equality problems in nonlinear norm inequalities, Yamada's unified and generalized inequalities for Opial's inequalities and the concrete and explicit integral representation of the implicit functions are presented.

Sklep: Libristo.pl

### Sparse Image and Signal Processing Cambridge University Press

**Książki / Literatura obcojęzyczna**

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB

Sklep: Libristo.pl

### Analog and Digital Signal Analysis Springer, Berlin

**Książki / Literatura obcojęzyczna**

This book provides comprehensive, graduate-level treatment of analog and digital signal analysis suitable for course use and self-guided learning. This expert text guides the reader from the basics of signal theory through a range of application tools for use in acoustic analysis, geophysics, and data compression. Each concept is introduced and explained step by step, and the necessary mathematical formulae are integrated in an accessible and intuitive way. The first part of the book explores how analog systems and signals form the basics of signal analysis. This section covers Fourier series and integral transforms of analog signals, Laplace and Hilbert transforms, the main analog filter classes, and signal modulations. Part II covers digital signals, demonstrating their key advantages. It presents z and Fourier transforms, digital filtering, inverse filters, deconvolution, and parametric modeling for deterministic signals. Wavelet decomposition and reconstruction of non-stationary signals are also discussed. The third part of the book is devoted to random signals, including spectral estimation, parametric modeling, and Tikhonov regularization. It covers statistics of one and two random variables and the principles and methods of spectral analysis. Estimation of signal properties is discussed in the context of ergodicity conditions and parameter estimations, including the use of Wiener and Kalman filters. Two appendices cover the basics of integration in the complex plane and linear algebra. A third appendix presents a basic Matlab toolkit for computer signal analysis. This expert text provides both a solid theoretical understanding and tools for real-world applications.

Sklep: Libristo.pl

Sklepy zlokalizowane w miastach: Warszawa, Kraków, Łódź, Wrocław, Poznań, Gdańsk, Szczecin, Bydgoszcz, Lublin, Katowice

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