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Nonlinear Dimensionality Reduction

summary of facts and concepts relating to well-known methods as well as recent developments in nonlinear dimensionality reduction. Methods are all;

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Nonlinear Dimensionality Reduction

summary of facts and concepts relating to well-known methods as well as recent developments in nonlinear dimensionality reduction. Methods are all;

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Nonlinear Dimensionality Reduction Techniques

through Dimensionality Reduction. Performing diagnosis of an energy system requires identifying relations between observed monitoring variables;

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Nonlinear Reaction-Diffusion-Convection Equations

), notably for their reduction to those of lower dimensionality (e.g. to ODEs) and constructing exact solutions. This book is devoted to (1) search;

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Multilinear Subspace Learning

efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the;

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Nonlinear Time Series

to solve dimensionality reduction problems arising from using fully nonparametric models and methods. Answering the call for an up-to-date;

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Nonlinear Time Series

to solve dimensionality reduction problems arising from using fully nonparametric models and methods. Answering the call for an up-to-date;

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Manifold Learning Theory and Applications

includes discussions on the relevance to nonlinear dimensionality reduction, clustering, graph-based subspace learning, spectral learning and;

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Nonlinear Model Reduction by Moment Matching

reduction for nonlinear systems or, at least, not as complete as the theory developed for linear systems. This monograph presents, in a uniform and;

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A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems

This book collects recent developments in nonlinear and complex systems. It provides up-to-date theoretic developments and new techniques;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

of improvements in building chaotic models are presented: nonlinear time series analysis, multi-step prediction, phase space dimensionality reduction;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

of improvements in building chaotic models are presented: nonlinear time series analysis, multi-step prediction, phase space dimensionality reduction;

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Machine Learning for Protein Subcellular Localization Prediction

extraction, and dimensionality reduction.;

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Pattern Recognition Algorithms for Data Mining

concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with;

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Pattern Recognition Algorithms for Data Mining

concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with;

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Robust Methods for Data Reduction

book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second;

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Robust Methods for Data Reduction

illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on;

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Disaster Risk Reduction and Resilience

-dimensionality of resilience and the various different perspectives in regards to disaster risk reduction are taken explicitly into account by providing;

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Reduced Order Methods for Modeling and Computational Reduction

reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and;

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Tensor Networks for Dimensionality Reduction and Large-scale Optimization

emerging tools for dimensionality reduction and large scale optimization. This monograph provides a systematic and example-rich guide to the basic;

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Neural Networks for Applied Sciences and Engineering

forecasting Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction;

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Fundamentals of Data Analytics

and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral;

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Fundamentals of Data Analytics

and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral;

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Nonlinear Control of Dynamic Networks

Significant progress has been made on nonlinear control systems in the past two decades. However, many of the existing nonlinear control;

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Nonlinear Control of Dynamic Networks

Significant progress has been made on nonlinear control systems in the past two decades. However, many of the existing nonlinear control;

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Simulated Evolution and Learning

developments; feature selection and dimensionality reduction; dynamic and uncertain environments; real-world applications; adaptive systems; and swarm;

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Nonlinear Principal Component Analysis and Its Applications

any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the;

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