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Digital Signal Processing with Kernel Methods

of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep;

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Kernel Learning Algorithms for Face Recognition

kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and;

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Kernel Mean Embedding of Distributions

learning. Kernel Mean Embedding of Distributions: A Review and Beyond provides a comprehensive review of existing work and recent advances in this;

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Kernel Methods for Remote Sensing Data Analysis

Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have;

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Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large;

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Kernels For Structured Data

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be;

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Kernel Methods And Machine Learning

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides;

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Generalized Mercer Kernels and Reproducing Kernel Banach Spaces

kernel Hilbert spaces (RKHSs). A key point is to endow Banach spaces with reproducing kernels such that machine learning in RKBSs can be well;

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Data-Variant Kernel Analysis

, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the;

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Machine Learning with Quantum Computers

quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from;

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Linear Algebra and Optimization for Machine Learning

analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier;

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Linear Algebra and Optimization for Machine Learning

analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier;

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Kernel Methods for Pattern Analysis

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data;

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Machine Learning and Knowledge Discovery in Databases

; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and;

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Introduction to Machine Learning

, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing;

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Machine Learning and Knowledge Discovery in Databases

; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods;

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Machine Learning and Knowledge Discovery in Databases

; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods;

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Statistical and Machine Learning Approaches for Network Analysis

weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross;

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Machine Learning and Knowledge Discovery in Databases

meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and;

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Machine Learning and Knowledge Discovery in Databases

meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and;

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Machine Learning and Knowledge Discovery in Databases

learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural;

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Kernel-based Approximation Methods Using Matlab

machine learning with the flexible and high-order tools developed using kernels, the authors explore their historical context and explain recent;

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Machine Learning

of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the;

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Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications;

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Machine Learning

learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the;

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